{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 卷积神经网络（Convolutional Neural Network, CNN）\n",
    "\n",
    "## 项目：实现一个狗品种识别算法\n",
    "\n",
    "在这个项目的最后，能够根据提供的任何一个图像作为输入。如果可以从图像中检测到一只狗，它会输出对狗品种的预测。如果图像中是一个人脸，它会预测一个与其最相似的狗的种类。下面这张图展示了完成项目后可能的输出结果。\n",
    "\n",
    "![Sample Dog Output](images/sample_dog_output.png)\n",
    "\n",
    "用来预测狗种类的算法会与预测人脸的算法不同，需要拼凑这两个模型来完成不同的任务。\n",
    "\n",
    "### 项目内容\n",
    "\n",
    "这个notebook分为不同的步骤。\n",
    "\n",
    "* [Step 0](#step0): 导入数据集\n",
    "* [Step 1](#step1): 检测人脸\n",
    "* [Step 2](#step2): 检测狗狗\n",
    "* [Step 3](#step3): 从头创建一个CNN来分类狗品种\n",
    "* [Step 4](#step4): 使用迁移学习来分类狗的品种1\n",
    "* [Step 5](#step5): 建立迁移学习来分类狗的品种2\n",
    "* [Step 6](#step6): 完成你的算法\n",
    "* [Step 7](#step7): 测试你的算法\n",
    "\n",
    "---\n",
    "\n",
    "<a id='step0'></a>\n",
    "## 步骤 0: 导入数据集\n",
    "\n",
    "### 导入狗数据集\n",
    "导入一个狗图像的数据集。使用 scikit-learn 库中的 `load_files` 函数来获取一些变量：\n",
    "- `train_files`, `valid_files`, `test_files` - 包含图像的文件路径的numpy数组\n",
    "- `train_targets`, `valid_targets`, `test_targets` - 包含独热编码分类标签的numpy数组\n",
    "- `dog_names` - 由字符串构成的与标签相对应的狗的种类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 133 total dog categories.\n",
      "There are 8351 total dog images.\n",
      "\n",
      "There are 6680 training dog images.\n",
      "There are 835 validation dog images.\n",
      "There are 836 test dog images.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_files       \n",
    "from keras.utils import np_utils\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "\n",
    "# load datasets\n",
    "def load_dataset(path):\n",
    "    data = load_files(path)\n",
    "    dog_files = np.array(data['filenames'])\n",
    "    # one hot encode\n",
    "    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n",
    "    return dog_files, dog_targets\n",
    "\n",
    "# load train, test, and validation datasets\n",
    "train_files, train_targets = load_dataset('/data/dog_images/train')\n",
    "valid_files, valid_targets = load_dataset('/data/dog_images/valid')\n",
    "test_files, test_targets = load_dataset('/data/dog_images/test')\n",
    "\n",
    "# load list of dog names\n",
    "dog_names = [item[20:-1] for item in sorted(glob(\"/data/dog_images/train/*/\"))]\n",
    "\n",
    "# print statistics about the dataset\n",
    "print('There are %d total dog categories.' % len(dog_names))\n",
    "print('There are %s total dog images.\\n' % len(np.hstack([train_files, valid_files, test_files])))\n",
    "print('There are %d training dog images.' % len(train_files))\n",
    "print('There are %d validation dog images.' % len(valid_files))\n",
    "print('There are %d test dog images.'% len(test_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入人脸数据集\n",
    "\n",
    "导入人脸图像数据集，文件所在路径存储在名为 `human_files` 的 numpy 数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 13233 total human images.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(8675309)\n",
    "\n",
    "# 加载打乱后的人脸数据集的文件名\n",
    "human_files = np.array(glob(\"/data/lfw/*/*\"))\n",
    "random.shuffle(human_files) # 只第一维度进行随机打乱\n",
    "\n",
    "print('There are %d total human images.' % len(human_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step1'></a>\n",
    "## 步骤1：检测人脸\n",
    " \n",
    "使用 OpenCV 中的 [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) 来检测图像中的人脸。OpenCV 提供了很多预训练的人脸检测模型，它们以XML文件保存在 [github](https://github.com/opencv/opencv/tree/master/data/haarcascades)。下载其中一个检测模型，把它存储在 `haarcascades` 的目录中，然后使用这个检测模型在样本图像中找到人脸。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of faces detected: 1\n",
      "[[ 70  68 114 114]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd850d18ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2                \n",
    "import matplotlib.pyplot as plt                        \n",
    "%matplotlib inline                               \n",
    "\n",
    "# 提取预训练的人脸检测模型\n",
    "face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')\n",
    "\n",
    "# 加载彩色图像，通道顺序为BGR\n",
    "img = cv2.imread(human_files[0])\n",
    "\n",
    "# 灰度处理\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# 在图像中找出脸\n",
    "faces = face_cascade.detectMultiScale(gray)\n",
    "\n",
    "# 打印图像中检测到的脸的个数\n",
    "print('Number of faces detected:', len(faces))\n",
    "print(faces)\n",
    "\n",
    "# 获取每一个所检测到的脸的识别框\n",
    "for (x,y,w,h) in faces:\n",
    "    # 在人脸图像中绘制出识别框\n",
    "    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "\n",
    "# 将BGR图像转变为RGB图像以打印\n",
    "cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# 展示含有识别框的图像\n",
    "plt.imshow(cv_rgb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OpenCV读取的彩色图像是**BGR**格式，Matplotlib显示彩色图像是按照**RGB**格式，因此如果使用OpenCV读取图像，则可能无法使用Matplotlib进行正常彩色的显示，所以需要对读取的图像数据进行转换。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在使用任何一个检测模型之前，通常要将图像转换为灰度图。`detectMultiScale` 函数使用储存在 `face_cascade` 中的的数据，对输入的灰度图像进行分类。\n",
    "\n",
    "在上方的代码中，`faces` 以 numpy 数组的形式保存识别到的面部信息。它其中每一行表示一个被检测到的脸，该数据包括如下四个信息：前两个元素  `x`、`y` 代表识别框左上角的 x 和 y 坐标；后两个元素代表识别框在 x 和 y 轴两个方向延伸的长度 `w` 和 `d`。 \n",
    "\n",
    "### 写一个人脸识别器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如果img_path路径表示的图像检测到了脸，返回\"True\" \n",
    "def face_detector(img_path):\n",
    "    img = cv2.imread(img_path)\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_cascade.detectMultiScale(gray)\n",
    "    return len(faces) > 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评估人脸检测模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "human: 100.00%\n",
      "dog: 11.00%\n"
     ]
    }
   ],
   "source": [
    "# 从每个数据集中提取前100个图像的文件路径\n",
    "human_files_short = human_files[:100]\n",
    "dog_files_short = train_files[:100]\n",
    "\n",
    "## 基于human_files_short和dog_files_short中的图像测试face_detector的表现\n",
    "# print(human_files_short[0]) # /data/lfw/Rick_Dinse/Rick_Dinse_0002.jpg\n",
    "\n",
    "def detect(detector, files):\n",
    "    return sum([1 if detector(img_path) else 0 for img_path in files]) / len(files)\n",
    "\n",
    "print('human: {:.2%}'.format(detect(face_detector, human_files_short)))\n",
    "print('dog: {:.2%}'.format(detect(face_detector, dog_files_short)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用OpenCV提供的分类模型进行人脸检测的结果如下：  \n",
    "- `human_files` 的前100张图像中，能够检测到**人脸**的图像占比为100%。  \n",
    "- `dog_files` 的前100张图像中，能够检测到**人脸**的图像占比为11%。\n",
    "\n",
    "可见该模型对人脸的检测效果较好，对非人脸有一定的误识率，即假阳性。  \n",
    "理想情况下，人图像中检测到人脸的概率应当为100%，而狗图像中检测到人脸的概率应该为0%。这个算法准确率并不是特别高，但结果仍然可以接受。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step2'></a>\n",
    "\n",
    "## 步骤 2: 检测狗狗\n",
    "\n",
    "使用预训练的残差网络模型 [ResNet-50](http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006) 来检测图像中的狗。先下载 ResNet-50 模型的网络结构参数，以及基于 [ImageNet](http://www.image-net.org/) 数据集的预训练权重。\n",
    "\n",
    "ImageNet 这目前一个非常流行的数据集，常被用来测试图像分类等计算机视觉任务相关的算法。它包含超过一千万个 URL，每一个都链接到 [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 中所对应的一个物体的图像。输入一个图像，该 ResNet-50 模型会返回一个对图像中物体的预测结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import ResNet50\n",
    "\n",
    "# 定义ResNet50模型\n",
    "ResNet50_model = ResNet50(weights='imagenet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理\n",
    "\n",
    "- 在使用 TensorFlow 作为后端的时候，在 Keras 中，CNN 的输入是一个4维数组（也被称作4维张量），它的各维度尺寸为 `(nb_samples, rows, columns, channels)`。其中 `nb_samples` 表示图像（或者样本）的总数，`rows`, `columns`, 和 `channels` 分别表示图像的行数、列数和通道数。\n",
    "\n",
    "\n",
    "- 下方的 `path_to_tensor` 函数实现如下：将彩色图像的文件路径作为输入，返回一个4维张量，来作为 Keras CNN 的输入。因为输入图像是彩色图像，因此它们具有三个通道（ channels 为 3）。\n",
    "    1. 该函数首先读取一张图像，然后将其缩放为 224×224 的图像。\n",
    "    2. 随后，该图像被调整为具有4个维度的张量。\n",
    "    3. 对于任一输入图像，最后返回的张量的维度是：`(1, 224, 224, 3)`。\n",
    "\n",
    "\n",
    "- `paths_to_tensor` 函数将图像路径的字符串组成的 numpy 数组作为输入，并返回一个4维张量，各维度尺寸为 `(nb_samples, 224, 224, 3)`。 在这里，`nb_samples`是提供的图像路径的数据中的样本数量或图像数量。你也可以将 `nb_samples` 理解为数据集中3维张量的个数（每个3维张量表示一个不同的图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image                  \n",
    "from tqdm import tqdm # 显示循环的进度条\n",
    "\n",
    "def path_to_tensor(img_path):\n",
    "    # 用PIL加载RGB图像为PIL.Image.Image类型\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    # 将PIL.Image.Image类型转化为3维张量(224, 224, 3)\n",
    "    x = image.img_to_array(img)\n",
    "    # 将3维张量转化为4维张量(1, 224, 224, 3)并返回\n",
    "    # return np.expand_dims(x, axis=0)\n",
    "    return x.reshape((1,) + x.shape)\n",
    "\n",
    "def paths_to_tensor(img_paths):\n",
    "    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n",
    "    return np.vstack(list_of_tensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基于 ResNet-50 架构进行预测\n",
    "\n",
    "得到四维张量后，把它们输入到 ResNet-50 网络、或 Keras 中其他类似的预训练模型之前，还需要进行一些额外的处理：\n",
    "1. 首先，需要把这些图像的通道顺序由 RGB转换为 BGR。\n",
    "2. 其次，预训练模型的输入都进行了额外的归一化过程。因此这里也要对这些张量进行归一化，即对所有图像所有像素都减去像素均值 `[103.939, 116.779, 123.68]`（以 RGB 模式表示，根据所有的 ImageNet 图像算出）。\n",
    "\n",
    "导入的 `preprocess_input` 函数实现了这些功能。可以在 [这里](https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py) 查看 `preprocess_input`的代码。\n",
    "\n",
    "实现了图像处理的部分后，使用模型的 `predict` 方法来实现预测，返回一个向量，向量的第 i 个元素表示该图像属于第 i 个 ImageNet 类别的概率。\n",
    "\n",
    "再对预测出的向量用 **argmax** 函数得到一个具有最大概率值的下标，即模型预测到的物体的类别。进而根据这个 [清单](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 能够知道这具体是哪个品种的狗狗。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "\n",
    "def ResNet50_predict_labels(img_path):\n",
    "    img = preprocess_input(path_to_tensor(img_path))\n",
    "    return np.argmax(ResNet50_model.predict(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 完成狗检测模型\n",
    "\n",
    "\n",
    "该 [清单](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 中的狗类别对应的序号为151-268。因此，在检查预训练模型判断图像是否包含狗的时候，只需要检查如上的 `ResNet50_predict_labels` 函数是否返回一个介于151和268之间（包含区间端点）的值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dog_detector(img_path):\n",
    "    prediction = ResNet50_predict_labels(img_path)\n",
    "    return ((prediction <= 268) & (prediction >= 151)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "human: 0.00%\n",
      "dog: 100.00%\n"
     ]
    }
   ],
   "source": [
    "# 测试dog_detector函数在human_files_short和dog_files_short的表现\n",
    "\n",
    "human_files_short = human_files[:100]\n",
    "dog_files_short = train_files[:100]\n",
    "\n",
    "# Test the performance of the face_detector algorithm \n",
    "# on the images in human_files_short and dog_files_short.\n",
    "\n",
    "def detect(detector, files):\n",
    "    return sum([1 if detector(img_path) else 0 for img_path in files]) / len(files)\n",
    "\n",
    "print('human: {:.2%}'.format(detect(dog_detector, human_files_short)))\n",
    "print('dog: {:.2%}'.format(detect(dog_detector, dog_files_short)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用预训练的 ResNet-50 模型来检测图像中的狗的结果如下：  \n",
    "- `human_files_short`中图像检测到狗狗的百分比为0。\n",
    "- `dog_files_short`中图像检测到狗狗的百分比为100%。\n",
    "\n",
    "可见该模型不管图片中是否包含狗狗，其检测准确性都非常高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='step3'></a>\n",
    "\n",
    "## 步骤 3: 从头开始创建一个CNN来分类狗品种\n",
    "\n",
    "现在能够在图像中识别人类及狗狗，然后对狗的类别进行识别。先__从头实现__一个卷积神经网络来对狗的品种进行分类。\n",
    "\n",
    "Keras 提供了能够轻松预测每次迭代（epoch）花费时间所需的函数，可以据此推断算法所需的训练时间。\n",
    "\n",
    "值得注意的是，对狗的图像进行分类是一项极具挑战性的任务。因为即便是一个正常人，也很难区分布列塔尼犬和威尔士史宾格犬。\n",
    "\n",
    "布列塔尼犬（Brittany） | 威尔士史宾格犬（Welsh Springer Spaniel）\n",
    "- | - \n",
    "<img src=\"images/Brittany_02625.jpg\" width=\"100\"> | <img src=\"images/Welsh_springer_spaniel_08203.jpg\" width=\"200\">\n",
    "\n",
    "不难发现其他的狗品种会有很小的类间差别（比如金毛寻回犬和美国水猎犬）。\n",
    "\n",
    "\n",
    "金毛寻回犬（Curly-Coated Retriever） | 美国水猎犬（American Water Spaniel）\n",
    "- | -\n",
    "<img src=\"images/Curly-coated_retriever_03896.jpg\" width=\"200\"> | <img src=\"images/American_water_spaniel_00648.jpg\" width=\"200\">\n",
    "\n",
    "同样，拉布拉多犬（labradors）有黄色、棕色和黑色这三种。那么基于视觉的算法将不得不克服这种较高的类间差别，以达到能够将这些不同颜色的同类狗分到同一个品种中。\n",
    "\n",
    "黄色拉布拉多犬（Yellow Labrador） | 棕色拉布拉多犬（Chocolate Labrador） | 黑色拉布拉多犬（Black Labrador）\n",
    "- | - | -\n",
    "<img src=\"images/Labrador_retriever_06457.jpg\" width=\"150\"> | <img src=\"images/Labrador_retriever_06455.jpg\" width=\"240\"> | <img src=\"images/Labrador_retriever_06449.jpg\" width=\"220\">\n",
    "\n",
    "不考虑品种略有失衡的影响，随机猜测到正确品种的概率是1/133，相对应的准确率是低于1%的。\n",
    "\n",
    "### 数据预处理\n",
    "\n",
    "通过对每张图像的像素值除以255，对图像实现了归一化处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6680/6680 [01:13<00:00, 90.30it/s] \n",
      "100%|██████████| 835/835 [00:08<00:00, 102.21it/s]\n",
      "100%|██████████| 836/836 [00:08<00:00, 102.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6680, 224, 224, 3)\n",
      "(835, 224, 224, 3)\n",
      "(836, 224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "from PIL import ImageFile\n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
    "\n",
    "# Keras中的数据预处理过程\n",
    "train_tensors = paths_to_tensor(train_files).astype('float32')/255\n",
    "valid_tensors = paths_to_tensor(valid_files).astype('float32')/255\n",
    "test_tensors = paths_to_tensor(test_files).astype('float32')/255\n",
    "\n",
    "print(train_tensors.shape)\n",
    "print(valid_tensors.shape)\n",
    "print(test_tensors.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型\n",
    "\n",
    "我搭建的卷积网络构成及其作用：  \n",
    "* 卷积层：提取图像特征，得到feature map堆栈。Filter更多的卷积层，可以提取到更多的特征。  \n",
    "* 最大池化层：降低每个feature map的维度（高和宽），只保留最显著的特征。  \n",
    "* 全局平均池化层：将最后一层卷积得到的feature map堆栈映射为向量，即每个feature map映射为一个节点。  \n",
    "* Max pooling和Global Average Pooling都可以保证特征的位置与旋转不变性，减少模型参数数量，减少过拟合问题。  \n",
    "* Batch normalization layer：用来解决 Covariate Shift 的问题，并加速运算过程。  \n",
    "* Dropout layer：用来降低模型复杂度，增强模型的泛化能力，防止过拟合，顺带降低了运算量。  \n",
    "* 全连接层：将特征输入到softmax层中进行分类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 224, 224, 16)      208       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 112, 112, 16)      0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 112, 112, 16)      64        \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 112, 112, 32)      2080      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 56, 56, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 56, 56, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 56, 56, 64)        8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 28, 28, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 28, 28, 128)       32896     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 133)               17157     \n",
      "=================================================================\n",
      "Total params: 61,045\n",
      "Trainable params: 60,821\n",
      "Non-trainable params: 224\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.models import Sequential\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(224,224,3)))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(filters=128, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(GlobalAveragePooling2D())\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "# 查看模型的信息\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 编译模型\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 回调函数 callbacks\n",
    "\n",
    "使用回调函数来观察训练过程中网络内部的状态和统计信息。通过传递回调函数列表到模型的 **.fit()** 中，可在给定的训练阶段调用该函数集中的函数。  ModelCheckpoint, EarlyStopping 都属于回调函数的一种。  \n",
    "[Keras' callback earlystopping](http://keras-cn.readthedocs.io/en/latest/other/callbacks/#earlystopping)  \n",
    "[Keras' callback modelcheckpoint](http://keras-cn.readthedocs.io/en/latest/other/callbacks/#modelcheckpoint)  \n",
    "[Keras' callback history](http://keras-cn.readthedocs.io/en/latest/other/callbacks/#history)  \n",
    "\n",
    "\n",
    "**earlystopping**\n",
    "\n",
    "在有些时候继续训练会导致测试集上的准确率下降。  \n",
    "继续训练导致测试准确率下降的原因可能是：1.过拟合 2.学习率过大导致不收敛 3.使用正则项时，Loss的减少可能不是因为准确率增加导致的，而是因为权重大小的降低。\n",
    "\n",
    "让算法自动选择epoch参数，并且避免epoch过多造成过拟合，可以使用Keras中提供的 **early stopping callback（提前结束）**方法。  \n",
    "early stopping 可以基于一些指定的规则自动提前结束训练过程，比如说连续指定次数epoch验证集准确率或误差都没有优化等。使用EarlyStopping可以加快学习的速度，提高调参效率。  \n",
    "\n",
    "`keras.callbacks.EarlyStopping(monitor='val_loss', patience=0, verbose=0, mode='auto')`  \n",
    "当监测值`val_loss`不再改善时，该回调函数将中止训练。\n",
    "\n",
    "参数：  \n",
    "\n",
    "* monitor：需要监视的量，有`val_acc`和`val_loss`。\n",
    "* patience：当early stop被激活（如发现loss相比上一个epoch训练没有下降），则经过patience个epoch后停止训练。\n",
    "* verbose：信息展示模式\n",
    "* mode：‘auto’，‘min’，‘max’之一，在min模式下，如果检测值停止下降则中止训练。在max模式下，当检测值不再上升则停止训练。\n",
    "\n",
    "**modelcheckpoint**\n",
    "\n",
    "使用模型检查点（model checkpointing）来储存具有最低验证集 loss 的模型。\n",
    "\n",
    "**History**\n",
    "\n",
    "```python\n",
    "keras.callbacks.History()\n",
    "```\n",
    "Callback that records events into a `History` object.\n",
    "该回调函数在Keras模型上会被自动调用，`History`对象即为`fit`方法的返回值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型\n",
    "\n",
    "对训练集进行 [数据增强](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)，来优化模型的表现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "Epoch 00001: val_loss improved from inf to 4.88740, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 53s - loss: 4.9113 - acc: 0.0102 - val_loss: 4.8874 - val_acc: 0.0144\n",
      "Epoch 2/30\n",
      "Epoch 00002: val_loss improved from 4.88740 to 4.87518, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 51s - loss: 4.7904 - acc: 0.0228 - val_loss: 4.8752 - val_acc: 0.0144\n",
      "Epoch 3/30\n",
      "Epoch 00003: val_loss improved from 4.87518 to 4.69206, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 52s - loss: 4.6974 - acc: 0.0305 - val_loss: 4.6921 - val_acc: 0.0311\n",
      "Epoch 4/30\n",
      "Epoch 00004: val_loss improved from 4.69206 to 4.54844, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 52s - loss: 4.6038 - acc: 0.0379 - val_loss: 4.5484 - val_acc: 0.0503\n",
      "Epoch 5/30\n",
      "Epoch 00005: val_loss did not improve\n",
      " - 52s - loss: 4.5290 - acc: 0.0439 - val_loss: 4.6293 - val_acc: 0.0383\n",
      "Epoch 6/30\n",
      "Epoch 00006: val_loss did not improve\n",
      " - 52s - loss: 4.4542 - acc: 0.0454 - val_loss: 4.7814 - val_acc: 0.0299\n",
      "Epoch 7/30\n",
      "Epoch 00007: val_loss improved from 4.54844 to 4.47959, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 52s - loss: 4.3683 - acc: 0.0567 - val_loss: 4.4796 - val_acc: 0.0587\n",
      "Epoch 8/30\n",
      "Epoch 00008: val_loss improved from 4.47959 to 4.32502, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 51s - loss: 4.2994 - acc: 0.0623 - val_loss: 4.3250 - val_acc: 0.0647\n",
      "Epoch 9/30\n",
      "Epoch 00009: val_loss did not improve\n",
      " - 52s - loss: 4.2276 - acc: 0.0672 - val_loss: 4.3546 - val_acc: 0.0515\n",
      "Epoch 10/30\n",
      "Epoch 00010: val_loss improved from 4.32502 to 4.26020, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 52s - loss: 4.1615 - acc: 0.0708 - val_loss: 4.2602 - val_acc: 0.0647\n",
      "Epoch 11/30\n",
      "Epoch 00011: val_loss improved from 4.26020 to 4.14463, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 51s - loss: 4.0860 - acc: 0.0847 - val_loss: 4.1446 - val_acc: 0.0778\n",
      "Epoch 12/30\n",
      "Epoch 00012: val_loss did not improve\n",
      " - 51s - loss: 4.0485 - acc: 0.0882 - val_loss: 4.1780 - val_acc: 0.0743\n",
      "Epoch 13/30\n",
      "Epoch 00013: val_loss did not improve\n",
      " - 51s - loss: 3.9772 - acc: 0.1006 - val_loss: 4.9273 - val_acc: 0.0479\n",
      "Epoch 14/30\n",
      "Epoch 00014: val_loss did not improve\n",
      " - 51s - loss: 3.9522 - acc: 0.1065 - val_loss: 4.1654 - val_acc: 0.0850\n",
      "Epoch 15/30\n",
      "Epoch 00015: val_loss improved from 4.14463 to 4.02528, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 51s - loss: 3.8663 - acc: 0.1157 - val_loss: 4.0253 - val_acc: 0.0982\n",
      "Epoch 16/30\n",
      "Epoch 00016: val_loss did not improve\n",
      " - 52s - loss: 3.8400 - acc: 0.1257 - val_loss: 4.2096 - val_acc: 0.0934\n",
      "Epoch 17/30\n",
      "Epoch 00017: val_loss did not improve\n",
      " - 52s - loss: 3.7834 - acc: 0.1256 - val_loss: 4.0730 - val_acc: 0.0982\n",
      "Epoch 18/30\n",
      "Epoch 00018: val_loss improved from 4.02528 to 4.01317, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 52s - loss: 3.7278 - acc: 0.1364 - val_loss: 4.0132 - val_acc: 0.1281\n",
      "Epoch 19/30\n",
      "Epoch 00019: val_loss improved from 4.01317 to 3.91894, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 51s - loss: 3.6967 - acc: 0.1396 - val_loss: 3.9189 - val_acc: 0.1234\n",
      "Epoch 20/30\n",
      "Epoch 00020: val_loss did not improve\n",
      " - 52s - loss: 3.6259 - acc: 0.1455 - val_loss: 4.1950 - val_acc: 0.1078\n",
      "Epoch 21/30\n",
      "Epoch 00021: val_loss did not improve\n",
      " - 52s - loss: 3.6235 - acc: 0.1508 - val_loss: 4.3442 - val_acc: 0.1030\n",
      "Epoch 22/30\n",
      "Epoch 00022: val_loss improved from 3.91894 to 3.86796, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 52s - loss: 3.5914 - acc: 0.1579 - val_loss: 3.8680 - val_acc: 0.1210\n",
      "Epoch 23/30\n",
      "Epoch 00023: val_loss did not improve\n",
      " - 52s - loss: 3.5429 - acc: 0.1626 - val_loss: 4.3009 - val_acc: 0.0934\n",
      "Epoch 24/30\n",
      "Epoch 00024: val_loss did not improve\n",
      " - 51s - loss: 3.5126 - acc: 0.1617 - val_loss: 3.8768 - val_acc: 0.1449\n",
      "Epoch 25/30\n",
      "Epoch 00025: val_loss did not improve\n",
      " - 52s - loss: 3.5000 - acc: 0.1702 - val_loss: 3.9943 - val_acc: 0.1257\n",
      "Epoch 26/30\n",
      "Epoch 00026: val_loss improved from 3.86796 to 3.82800, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 52s - loss: 3.4502 - acc: 0.1739 - val_loss: 3.8280 - val_acc: 0.1341\n",
      "Epoch 27/30\n",
      "Epoch 00027: val_loss did not improve\n",
      " - 52s - loss: 3.4368 - acc: 0.1765 - val_loss: 3.9071 - val_acc: 0.1293\n",
      "Epoch 28/30\n",
      "Epoch 00028: val_loss improved from 3.82800 to 3.65066, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      " - 52s - loss: 3.4201 - acc: 0.1813 - val_loss: 3.6507 - val_acc: 0.1557\n",
      "Epoch 29/30\n",
      "Epoch 00029: val_loss did not improve\n",
      " - 52s - loss: 3.3963 - acc: 0.1883 - val_loss: 3.9449 - val_acc: 0.1485\n",
      "Epoch 30/30\n",
      "Epoch 00030: val_loss did not improve\n",
      " - 52s - loss: 3.3767 - acc: 0.1866 - val_loss: 3.8024 - val_acc: 0.1749\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd7f4b945c0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "# 设置训练模型的epochs的数量\n",
    "epochs = 30\n",
    "batch_size = 32\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1, mode='auto')\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', \n",
    "                               verbose=1, save_best_only=True) # 保存最佳交叉验证损失的模型权重\n",
    "\n",
    "# model.fit(train_tensors, train_targets, \n",
    "#           validation_data=(valid_tensors, valid_targets),\n",
    "#           epochs=epochs, batch_size=20, callbacks=[checkpointer, early_stopping], verbose=1)\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# 设置数据增强的参数\n",
    "datagen_train = ImageDataGenerator(\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    rotation_range=20,\n",
    "    vertical_flip=True,\n",
    "    horizontal_flip=True)\n",
    "\n",
    "# fit augmented image generator on data\n",
    "datagen_train.fit(train_tensors)\n",
    "\n",
    "# fits the model on batches with real-time data augmentation:\n",
    "model.fit_generator(datagen_train.flow(train_tensors, train_targets, batch_size=batch_size),\n",
    "                    steps_per_epoch=train_tensors.shape[0] // batch_size,\n",
    "                    epochs=epochs, verbose=2, callbacks=[checkpointer, early_stopping],\n",
    "                    validation_data=(valid_tensors, valid_targets),\n",
    "                    validation_steps=valid_tensors.shape[0] // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 加载具有最好验证loss的模型\n",
    "model.load_weights('saved_models/weights.best.from_scratch.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试模型\n",
    "\n",
    "在狗图像的测试数据集上对模型进行测试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 15.4306%\n"
     ]
    }
   ],
   "source": [
    "# 获取测试数据集中每一个图像所预测的狗品种的index\n",
    "dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n",
    "\n",
    "# 报告测试准确率\n",
    "test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**提高准确率的技巧：**  \n",
    "可以使劲往上加层，直到它在测试集上过拟合，然后再加正则化和数据增强等抑制过拟合。  \n",
    "如果不过拟合了，再接着往上加层。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step4'></a>\n",
    "## 步骤 4: 使用一个CNN来区分狗的品种\n",
    "\n",
    "\n",
    "使用 迁移学习（Transfer Learning）可以在不损失准确率的情况下大大减少训练时间。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 得到从图像中提取的特征向量（Bottleneck Features）\n",
    "\n",
    "把预训练好的模型看作一个固定的函数映射，让原始图像通过该函数映射得到特征向量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "bottleneck_features = np.load('/data/bottleneck_features/DogVGG16Data.npz')\n",
    "train_VGG16 = bottleneck_features['train']\n",
    "valid_VGG16 = bottleneck_features['valid']\n",
    "test_VGG16 = bottleneck_features['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型架构\n",
    "\n",
    "该模型使用预训练的 VGG-16 模型作为固定的图像特征提取器，其中 VGG-16 最后一层卷积层的输出被直接输入到我们的模型。我们只需要添加一个全局平均池化层以及一个全连接层，其中全连接层使用 softmax 激活函数，每一个狗的种类都包含一个节点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "global_average_pooling2d_2 ( (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 133)               68229     \n",
      "=================================================================\n",
      "Total params: 68,229\n",
      "Trainable params: 68,229\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "VGG16_model = Sequential()\n",
    "VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))\n",
    "VGG16_model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "VGG16_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 编译模型\n",
    "VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/30\n",
      "6480/6680 [============================>.] - ETA: 0s - loss: 12.0061 - acc: 0.1395Epoch 00001: val_loss improved from inf to 10.49096, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 290us/step - loss: 11.9936 - acc: 0.1409 - val_loss: 10.4910 - val_acc: 0.2395\n",
      "Epoch 2/30\n",
      "6480/6680 [============================>.] - ETA: 0s - loss: 9.9776 - acc: 0.2940Epoch 00002: val_loss improved from 10.49096 to 9.83896, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 238us/step - loss: 9.9490 - acc: 0.2954 - val_loss: 9.8390 - val_acc: 0.2910\n",
      "Epoch 3/30\n",
      "6460/6680 [============================>.] - ETA: 0s - loss: 9.4474 - acc: 0.3508Epoch 00003: val_loss improved from 9.83896 to 9.54049, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 248us/step - loss: 9.4250 - acc: 0.3518 - val_loss: 9.5405 - val_acc: 0.3257\n",
      "Epoch 4/30\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 9.1702 - acc: 0.3803Epoch 00004: val_loss improved from 9.54049 to 9.49595, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 245us/step - loss: 9.1697 - acc: 0.3805 - val_loss: 9.4959 - val_acc: 0.3389\n",
      "Epoch 5/30\n",
      "6520/6680 [============================>.] - ETA: 0s - loss: 9.0591 - acc: 0.4021Epoch 00005: val_loss improved from 9.49595 to 9.44110, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 246us/step - loss: 9.0415 - acc: 0.4031 - val_loss: 9.4411 - val_acc: 0.3509\n",
      "Epoch 6/30\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 8.9768 - acc: 0.4150Epoch 00006: val_loss improved from 9.44110 to 9.31303, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 245us/step - loss: 8.9618 - acc: 0.4159 - val_loss: 9.3130 - val_acc: 0.3629\n",
      "Epoch 7/30\n",
      "6540/6680 [============================>.] - ETA: 0s - loss: 8.8894 - acc: 0.4257Epoch 00007: val_loss improved from 9.31303 to 9.30936, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 246us/step - loss: 8.8875 - acc: 0.4256 - val_loss: 9.3094 - val_acc: 0.3593\n",
      "Epoch 8/30\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 8.8235 - acc: 0.4320Epoch 00008: val_loss improved from 9.30936 to 9.22911, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 247us/step - loss: 8.8121 - acc: 0.4329 - val_loss: 9.2291 - val_acc: 0.3689\n",
      "Epoch 9/30\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 8.6665 - acc: 0.4463Epoch 00009: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 243us/step - loss: 8.6628 - acc: 0.4464 - val_loss: 9.3539 - val_acc: 0.3569\n",
      "Epoch 10/30\n",
      "6520/6680 [============================>.] - ETA: 0s - loss: 8.5626 - acc: 0.4549Epoch 00010: val_loss improved from 9.22911 to 9.09764, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 246us/step - loss: 8.5853 - acc: 0.4534 - val_loss: 9.0976 - val_acc: 0.3749\n",
      "Epoch 11/30\n",
      "6480/6680 [============================>.] - ETA: 0s - loss: 8.4704 - acc: 0.4611Epoch 00011: val_loss improved from 9.09764 to 9.04133, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 248us/step - loss: 8.4625 - acc: 0.4617 - val_loss: 9.0413 - val_acc: 0.3569\n",
      "Epoch 12/30\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 8.3862 - acc: 0.4686Epoch 00012: val_loss improved from 9.04133 to 8.87261, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 246us/step - loss: 8.3689 - acc: 0.4698 - val_loss: 8.8726 - val_acc: 0.3784\n",
      "Epoch 13/30\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 8.2052 - acc: 0.4748Epoch 00013: val_loss improved from 8.87261 to 8.75615, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 246us/step - loss: 8.2205 - acc: 0.4738 - val_loss: 8.7561 - val_acc: 0.3964\n",
      "Epoch 14/30\n",
      "6540/6680 [============================>.] - ETA: 0s - loss: 8.1073 - acc: 0.4876Epoch 00014: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 246us/step - loss: 8.1236 - acc: 0.4867 - val_loss: 8.7859 - val_acc: 0.3952\n",
      "Epoch 15/30\n",
      "6520/6680 [============================>.] - ETA: 0s - loss: 8.1033 - acc: 0.4902Epoch 00015: val_loss improved from 8.75615 to 8.68314, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 247us/step - loss: 8.0904 - acc: 0.4910 - val_loss: 8.6831 - val_acc: 0.3964\n",
      "Epoch 16/30\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 8.0103 - acc: 0.4953Epoch 00016: val_loss improved from 8.68314 to 8.67002, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 248us/step - loss: 8.0080 - acc: 0.4955 - val_loss: 8.6700 - val_acc: 0.4048\n",
      "Epoch 17/30\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 7.9859 - acc: 0.5005Epoch 00017: val_loss improved from 8.67002 to 8.61892, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 249us/step - loss: 7.9808 - acc: 0.5006 - val_loss: 8.6189 - val_acc: 0.4096\n",
      "Epoch 18/30\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 7.9316 - acc: 0.5030Epoch 00018: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 243us/step - loss: 7.9412 - acc: 0.5022 - val_loss: 8.6235 - val_acc: 0.3988\n",
      "Epoch 19/30\n",
      "6480/6680 [============================>.] - ETA: 0s - loss: 7.9038 - acc: 0.5054Epoch 00019: val_loss improved from 8.61892 to 8.58104, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 247us/step - loss: 7.8934 - acc: 0.5060 - val_loss: 8.5810 - val_acc: 0.4036\n",
      "Epoch 20/30\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 7.8210 - acc: 0.5041Epoch 00020: val_loss improved from 8.58104 to 8.43653, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 246us/step - loss: 7.8270 - acc: 0.5039 - val_loss: 8.4365 - val_acc: 0.4132\n",
      "Epoch 21/30\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 7.7682 - acc: 0.5105Epoch 00021: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 243us/step - loss: 7.7672 - acc: 0.5105 - val_loss: 8.4478 - val_acc: 0.4251\n",
      "Epoch 22/30\n",
      "6480/6680 [============================>.] - ETA: 0s - loss: 7.7676 - acc: 0.5122Epoch 00022: val_loss improved from 8.43653 to 8.35560, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 2s 247us/step - loss: 7.7361 - acc: 0.5141 - val_loss: 8.3556 - val_acc: 0.4275\n",
      "Epoch 23/30\n",
      "6500/6680 [============================>.] - ETA: 0s - loss: 7.7257 - acc: 0.5162Epoch 00023: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 246us/step - loss: 7.7263 - acc: 0.5162 - val_loss: 8.4514 - val_acc: 0.4216\n",
      "Epoch 24/30\n",
      "6520/6680 [============================>.] - ETA: 0s - loss: 7.7044 - acc: 0.5183Epoch 00024: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 244us/step - loss: 7.7178 - acc: 0.5175 - val_loss: 8.4584 - val_acc: 0.4240\n",
      "Epoch 25/30\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 7.7229 - acc: 0.5182Epoch 00025: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 244us/step - loss: 7.7135 - acc: 0.5189 - val_loss: 8.4577 - val_acc: 0.4216\n",
      "Epoch 26/30\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 7.7070 - acc: 0.5204Epoch 00026: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 243us/step - loss: 7.7102 - acc: 0.5202 - val_loss: 8.5048 - val_acc: 0.4263\n",
      "Epoch 27/30\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 7.6997 - acc: 0.5214Epoch 00027: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 242us/step - loss: 7.7051 - acc: 0.5211 - val_loss: 8.3577 - val_acc: 0.4323\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00027: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd7fc44bef0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 训练模型\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='auto')\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "VGG16_model.fit(train_VGG16, train_targets, \n",
    "          validation_data=(valid_VGG16, valid_targets), verbose=1,\n",
    "          epochs=30, batch_size=20, callbacks=[checkpointer, early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 加载具有最好验证loss的模型\n",
    "VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试模型\n",
    "测试此CNN模型在狗图像测试数据集中识别品种的效果。\n",
    "\n",
    "可以发现训练时间大大减少，而且准确率提高了很多。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 43.5407%\n"
     ]
    }
   ],
   "source": [
    "# 获取测试数据集中每一个图像所预测的狗品种的index\n",
    "VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]\n",
    "\n",
    "# 报告测试准确率\n",
    "test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用模型预测狗的品种"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from extract_bottleneck_features import *\n",
    "\n",
    "def VGG16_predict_breed(img_path):\n",
    "    # 提取bottleneck特征\n",
    "    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))\n",
    "    # 获取预测向量\n",
    "    predicted_vector = VGG16_model.predict(bottleneck_feature)\n",
    "    # 返回此模型预测的狗的品种\n",
    "    return dog_names[np.argmax(predicted_vector)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step5'></a>\n",
    "## 步骤 5: 建立一个CNN来分类狗的品种\n",
    "\n",
    "使用迁移学习来建立一个CNN，从而可以从图像中识别狗的品种。在步骤4中，使用了迁移学习来创建一个基于 VGG-16 提取的特征向量来搭建一个 CNN。在本部分内容中，使用另一个预训练模型来搭建一个 CNN。为了让这个任务更易实现，已经预先对目前 keras 中可用的几种网络进行了预训练：\n",
    "\n",
    "- [VGG-19](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz) bottleneck features\n",
    "- [ResNet-50](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz) bottleneck features\n",
    "- [Inception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz) bottleneck features\n",
    "- [Xception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz) bottleneck features\n",
    "\n",
    "这些文件被命名为为：\n",
    "\n",
    "    Dog{network}Data.npz\n",
    "\n",
    "其中 `{network}` 可以是 `VGG19`、`Resnet50`、`InceptionV3` 或 `Xception` 中的一个。选择上方网络架构中的一个，下载相对应的bottleneck特征，并将所下载的文件保存在目录 `/data/bottleneck_features/` 中。\n",
    "\n",
    "### 获取模型的特征向量\n",
    "\n",
    "运行下方代码提取训练、测试与验证集相对应的bottleneck特征。\n",
    "```python\n",
    "bottleneck_features = np.load('/data/bottleneck_features/Dog{network}Data.npz')\n",
    "train_{network} = bottleneck_features['train']\n",
    "valid_{network} = bottleneck_features['valid']\n",
    "test_{network} = bottleneck_features['test']\n",
    "```\n",
    "当下比较主流的架构是**ResNet-50**和**Xception**，容易达到80%以上的准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6680, 1, 1, 2048)\n",
      "(835, 1, 1, 2048)\n",
      "(836, 1, 1, 2048)\n"
     ]
    }
   ],
   "source": [
    "### 从另一个预训练的CNN获取bottleneck特征\n",
    "\n",
    "bottleneck_features = np.load('/data/bottleneck_features/DogResnet50Data.npz')\n",
    "train_Resnet50 = bottleneck_features['train']\n",
    "valid_Resnet50 = bottleneck_features['valid']\n",
    "test_Resnet50 = bottleneck_features['test']\n",
    "\n",
    "print(train_Resnet50.shape)\n",
    "print(valid_Resnet50.shape)\n",
    "print(test_Resnet50.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义模型架构\n",
    "\n",
    "建立一个CNN来分类狗品种。\n",
    "\n",
    "我选择使用Resnet50网络架构，是因为Resnet50只是在CNN上面增加了跳过层级的连接，和CNN还是很相似的，但是其使梯度信号的传播路径更短，避免了梯度消失的影响。\n",
    "\n",
    "* 全局平均池化层：将网络模型`Resnet50`的输出作为输入，将其feature map堆栈映射为向量，即每个feature map映射为一个节点，可以防止过拟合。  \n",
    "* 全连接层：将特征输入到softmax层中进行分类。  \n",
    "* 这个CNN架构的分类效果比较好，因为这是在已经训练好的网络模型`Resnet50`的基础上添加我自己的分类输出层。\n",
    "* 在第三步中，要达到如模型`Resnet50`这样好的效果，需要使用大量的图像数据（如ImageNet），和足够深度的网络模型，并且训练特别长的时间。自己从头构建的一个CNN分类模型，模型深度不够，训练时间过短，导致特征提取不充分，所以准确率不高。\n",
    "* 在第四步中，`VGG16`模型的效果欠佳，是因为其深度远不及`Resnet50`模型的深度，同时`Resnet50`模型还采用残差网络解决网络过深出现的梯度消失问题，在没有给网络增加额外的参数和计算量的情况下，可以大大增加了模型的训练速度、提高了训练效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "global_average_pooling2d_11  (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 133)               272517    \n",
      "=================================================================\n",
      "Total params: 272,517\n",
      "Trainable params: 272,517\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras import regularizers\n",
    "\n",
    "Resnet50_model = Sequential()\n",
    "Resnet50_model.add(GlobalAveragePooling2D(input_shape=train_Resnet50.shape[1:]))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "Resnet50_model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "Resnet50_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TODO: 编译模型\n",
    "Resnet50_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/30\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 1.7087 - acc: 0.5920Epoch 00001: val_loss improved from inf to 0.84963, saving model to saved_models/weights.best.Resnet50.hdf5\n",
      "6680/6680 [==============================] - 4s 548us/step - loss: 1.7022 - acc: 0.5933 - val_loss: 0.8496 - val_acc: 0.7521\n",
      "Epoch 2/30\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.3482 - acc: 0.9130Epoch 00002: val_loss improved from 0.84963 to 0.71860, saving model to saved_models/weights.best.Resnet50.hdf5\n",
      "6680/6680 [==============================] - 2s 260us/step - loss: 0.3498 - acc: 0.9127 - val_loss: 0.7186 - val_acc: 0.7880\n",
      "Epoch 3/30\n",
      "6520/6680 [============================>.] - ETA: 0s - loss: 0.1638 - acc: 0.9690Epoch 00003: val_loss improved from 0.71860 to 0.63905, saving model to saved_models/weights.best.Resnet50.hdf5\n",
      "6680/6680 [==============================] - 2s 264us/step - loss: 0.1642 - acc: 0.9684 - val_loss: 0.6391 - val_acc: 0.8048\n",
      "Epoch 4/30\n",
      "6540/6680 [============================>.] - ETA: 0s - loss: 0.0896 - acc: 0.9881Epoch 00004: val_loss improved from 0.63905 to 0.60133, saving model to saved_models/weights.best.Resnet50.hdf5\n",
      "6680/6680 [==============================] - 2s 265us/step - loss: 0.0890 - acc: 0.9883 - val_loss: 0.6013 - val_acc: 0.8204\n",
      "Epoch 5/30\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0563 - acc: 0.9944Epoch 00005: val_loss improved from 0.60133 to 0.56970, saving model to saved_models/weights.best.Resnet50.hdf5\n",
      "6680/6680 [==============================] - 2s 263us/step - loss: 0.0562 - acc: 0.9943 - val_loss: 0.5697 - val_acc: 0.8347\n",
      "Epoch 6/30\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0373 - acc: 0.9967Epoch 00006: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 261us/step - loss: 0.0372 - acc: 0.9967 - val_loss: 0.5793 - val_acc: 0.8228\n",
      "Epoch 7/30\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0312 - acc: 0.9968Epoch 00007: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 261us/step - loss: 0.0311 - acc: 0.9969 - val_loss: 0.5833 - val_acc: 0.8263\n",
      "Epoch 8/30\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0267 - acc: 0.9973Epoch 00008: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 261us/step - loss: 0.0266 - acc: 0.9973 - val_loss: 0.5749 - val_acc: 0.8263\n",
      "Epoch 9/30\n",
      "6540/6680 [============================>.] - ETA: 0s - loss: 0.0208 - acc: 0.9979Epoch 00009: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 262us/step - loss: 0.0207 - acc: 0.9978 - val_loss: 0.6065 - val_acc: 0.8371\n",
      "Epoch 10/30\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0223 - acc: 0.9967Epoch 00010: val_loss improved from 0.56970 to 0.56409, saving model to saved_models/weights.best.Resnet50.hdf5\n",
      "6680/6680 [==============================] - 2s 262us/step - loss: 0.0223 - acc: 0.9967 - val_loss: 0.5641 - val_acc: 0.8419\n",
      "Epoch 11/30\n",
      "6520/6680 [============================>.] - ETA: 0s - loss: 0.0207 - acc: 0.9969Epoch 00011: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 262us/step - loss: 0.0206 - acc: 0.9969 - val_loss: 0.5934 - val_acc: 0.8359\n",
      "Epoch 12/30\n",
      "6540/6680 [============================>.] - ETA: 0s - loss: 0.0181 - acc: 0.9963Epoch 00012: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 261us/step - loss: 0.0183 - acc: 0.9963 - val_loss: 0.6748 - val_acc: 0.8156\n",
      "Epoch 13/30\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0127 - acc: 0.9977Epoch 00013: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 260us/step - loss: 0.0130 - acc: 0.9976 - val_loss: 0.6303 - val_acc: 0.8335\n",
      "Epoch 14/30\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 0.0273 - acc: 0.9945Epoch 00014: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 261us/step - loss: 0.0272 - acc: 0.9945 - val_loss: 0.7187 - val_acc: 0.8228\n",
      "Epoch 15/30\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0575 - acc: 0.9833Epoch 00015: val_loss did not improve\n",
      "6680/6680 [==============================] - 2s 261us/step - loss: 0.0575 - acc: 0.9834 - val_loss: 0.9750 - val_acc: 0.7725\n",
      "Epoch 00015: early stopping\n"
     ]
    }
   ],
   "source": [
    "epochs=30\n",
    "batch_size=32\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='auto')\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Resnet50.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "history = Resnet50_model.fit(train_Resnet50, train_targets,\n",
    "                   validation_data=(valid_Resnet50, valid_targets),\n",
    "                   epochs=30, batch_size=20, verbose=1,\n",
    "                   callbacks=[checkpointer, early_stopping])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Keras在训练期间可视化训练误差和验证误差**\n",
    "\n",
    "可视化训练过程中的loss曲线和accuracy曲线，具体参考[Display Deep Learning Model Training History in Keras](https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/)这篇文章，这样可以让训练过程更为直观，可以更方便地判断模型是否出现了欠拟合或过拟合。 \n",
    "\n",
    "`fit()` 方法会返回一个训练期间历史数据记录对象，包含 `training error`, `training accuracy`, `validation error`, `validation accuracy` 字段。\n",
    "\n",
    "```python\n",
    "history = model.fit()\n",
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "# output:\n",
    "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd640c5dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd6410e64a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载具有最佳验证loss的模型权重\n",
    "Resnet50_model.load_weights('saved_models/weights.best.Resnet50.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试模型\n",
    "\n",
    "在狗图像的测试数据集上测试模型，测试准确率大于80%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 84.0909%\n"
     ]
    }
   ],
   "source": [
    "### TODO: 在测试集上计算分类准确率\n",
    "\n",
    "Resnet50_predictions = [np.argmax(Resnet50_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Resnet50]\n",
    "\n",
    "test_accuracy = 100 * np.sum(np.array(Resnet50_predictions)==np.argmax(test_targets, axis=1))/len(Resnet50_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### 使用模型测试狗的品种\n",
    "\n",
    "实现一个函数，它的输入为图像路径，功能为预测对应图像的类别，输出为模型预测出的狗类别。\n",
    "\n",
    "与步骤5中的模拟函数类似，包含如下三个步骤：\n",
    "\n",
    "1. 根据选定的模型载入图像特征（bottleneck features）\n",
    "2. 将图像特征输输入到你的模型中，并返回预测向量。注意，在该向量上使用 argmax 函数可以返回狗种类的序号。\n",
    "3. 使用在步骤0中定义的 `dog_names` 数组来返回对应的狗种类名称。\n",
    "\n",
    "提取图像特征过程中使用到的函数可以在 `extract_bottleneck_features.py` 中找到。同时，它们应已在之前的代码块中被导入。根据选定的 CNN 网络，使用 `extract_{network}` 函数来获得对应的图像特征，其中 `{network}` 代表 `VGG19`, `Resnet50`, `InceptionV3`, 或 `Xception` 中的一个。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 该函数将图像的路径作为输入，返回此模型所预测的狗的品种\n",
    "\n",
    "from extract_bottleneck_features import *\n",
    "\n",
    "def Resnet50_predict_breed(img_path):\n",
    "    # extract bottleneck features\n",
    "    bottleneck_feature = extract_Resnet50(path_to_tensor(img_path))\n",
    "    # obtain predicted vector\n",
    "    predicted_vector = Resnet50_model.predict(bottleneck_feature)\n",
    "    # return dog breed that is predicted by the model\n",
    "    return dog_names[np.argmax(predicted_vector)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='step6'></a>\n",
    "## 步骤 6: 完成最终算法\n",
    "\n",
    "编写检测图像中人类与狗的函数，输入为图像的路径，能够区分图像是否包含一个人、狗或两者都不包含，使用上面的 `face_detector` 和 `dog_detector` 函数。\n",
    "\n",
    "- 如果从图像中检测到一只__狗__，返回被预测的品种。\n",
    "- 如果从图像中检测到__人__，返回最相像的狗品种。\n",
    "- 如果两者都不能在图像中检测到，输出错误提示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dog_recognition(img_path):\n",
    "    # 先判断dog后判断face，因为前者的准确率更高\n",
    "    if dog_detector(img_path) == True:\n",
    "        print('* This is a dog! Its breed is ...')\n",
    "    elif face_detector(img_path) == True:\n",
    "        print('* Hello human! You look like a ...')\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "    return Resnet50_predict_breed(img_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step7'></a>\n",
    "## 步骤 7: 测试算法\n",
    "\n",
    "使用任意至少6张图片，至少使用两张人类图片和两张狗的图片来测试算法。\n",
    "\n",
    "结论分析：\n",
    "1. 对于猫狗的预测都是正确的，对于找出和人最像的狗狗种类，感觉不是很像。\n",
    "2. 模型改进：\n",
    " * 检测人脸是使用OpenCV提供的分类器模型，该模型对于非人的图像的存在一定误差，所以检测人脸的模型也可以通过迁移学习来构建。  \n",
    " * 从loss图可以看出该模型有过拟合的情况，使用迁移学习时可以试着减少`Resnet50`模型的层数以减小模型的复杂度。  \n",
    " * 可以通过随机地平移或旋转已有图像数据以获取性能的提升，提高模型的泛化能力。  \n",
    " \n",
    " [what-dog](https://www.what-dog.net/#)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* This is not dog or human.\n",
      "* This is not dog or human.\n",
      "* This is a dog! Its breed is ...\n",
      "   in/016.Beagle\n",
      "* This is a dog! Its breed is ...\n",
      "   in/029.Border_collie\n",
      "* This is a dog! Its breed is ...\n",
      "   in/115.Papillon\n",
      "* Hello human! You look like a ...\n",
      "   in/063.English_springer_spaniel\n",
      "* Hello human! You look like a ...\n",
      "   in/127.Silky_terrier\n"
     ]
    },
    {
     "data": {
      "image/png": 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wbNlYv9gVds2mZrbAkLEzFUICPFkFkQq0L3N8i5d9ssbynh/Hn7EqmyQ44/11a89GHgmllLP9PTrqw6dX86k4QP1IPgF4cYgqSdbncfAiaJZV0NHZnLA6sQMSDk/WaGNXWZ37B7Rb3g/YXjPWkdceN+9MaTNpGmKK3L17l29+55s8fvKQ/f19vPcjgVFVRrjknEeCumkacs7cvXuXk5MTfDBFw/CdUfP4t8TVujOQM4fTCVVlhHbKmb4EbtZJp2EOXQ9Yl5gJ4EgoXoU4nl+JSXEx06l9T9u2JDKT2R71pCmKQIgxcnZ+Ssg13nv6ZGqiejoZ10k/CkGUSjyDumM5b8k5U/tA1/e89tprfPnLf8TPff4XeeaZZ5jNZqTRfsjYuFtXjW8SUzu8NTZtsEzXLYmuZzKdMj9bMNtrmEynLM7PeOm73+brX/8aR0ePeeONO5ycnPD4wSPOlwsWy+VoE4l3OI5oji14sThb8E/bJX/05f+Hz33uc9x/dJ+2bXn19dscnxxzuL/HZH+fJkXMDlF8EGKM9H1HEyYsFxERZbFY8OadVwthU7O/v0/bLbh14ybT6Yz9/UPoE9kp4oQk4FVIrMj5Hyd+kLH5A5E4WeAy3/0ylcBw3GBktW2LqPL0zRvUzvHGa69SBWGvFlQTfd8x2dsjNHv4yls0Pyg5ZhbdAhFh/8Y1YjdhcXaOrzxp2dlCkTLZefo+sjg/p2kqpr6hb1vA8Qs//wV+7/e+RJc6dC0ifJWTAoyRVTPIdBVlGUM1abCdYRBoXdI32QmDBxaKFFFJoMbQr7P742c0bvbvpY7sVQOxRI8GokqTidFEIBfjCHDejddzenpKzplP/dSn+KVf+iVu3brBc0/dYD6fE2NmuVxyfX/CM7ducO3f+g1efe17/NZv/dZ7OkEOqXMWPXLFeShRBMUUG96UOBYrVjRHixq7gIjDuUBKvZ1PKU5LgRhpsW78jfbShvRzFIIjayog8Y5tR2Pd2R0Zbzbv3LbDue1AD1h3Dt+JguEt+1I2h+rYt1mIJLz25OSI0cZhykvOj0546Zvf4P7t1zg/OyV1C3zw1NUEMsyaKa6uaGOiamqcz8S+x1GiKcOzFhP9Ys7t27d54Y3bzPb3mRzuMyiuBzWV3Qcx1dz7QP3448AGqXLV61sExeVkzeWRqPWxdhXWlTTD+ByMzeGfbp3bHAO3cexVSrHhO9Z/Xtae7baO8/YlJM72Mzf8vn7+FDedtuEa15V9Q3R9MCztOla/r5Q4eeO1H4fhmLP5oQMpsdSORetoW3MXnUtUmqmKpNgcZsGpRanM8QNxSqigwgNCEAfB1h9XkjqCOJRi/JMLyTIQWhSH1lSqKS+JaUmfIjF1ZO0RZ4aUzdFKn5eENCF1Lb02EDxNyKhXkvZob3PTxftTxkNOq3VVMq4yM2c41jmHtm1pm4yEmwSbn5e5J/WmsOpzYjKZFALFj4TM8NnR+dGS1r01pm0cmTIjpXTpuB9IHCjp4SS8qJHgzqEk6sbaEHzFoIZwMRCco1YhZyEpuGzKUVHQmMgkqKv1kfEDj613AxHFuwzOjUoOcRmhQlxCtSqEjvWR9x7Eo+LwAqKCEsHZOp/oEIzQo/TNoBjLgEomVEaUIB6npnZBTVZvgTuL5osTstjvQdzobCC2TpldJmXdtjQAg5JG4hLwgZwiPgxEZbFJsluRIgz2hT0HMWc8AZwpq4yBD4hOQIYAY4WKkrJDCDgcKUfA41wo/Igf26SacV7Q7MvflorgFZIIYAQY4nEpAg4tKTUpCioevN0j1OGc2U0kh4qRXJoitTfVRyaX9RksICZ40TFFcrinQgBJVK6x59IVi0yHIOoHF9sKnOE1YMOvQG3+CMEhKC9/5zv883/+z/jWt/6UnDOf/slPjGRy3/ccHBxQ1zXn5+emCCwEzksvvcTtN94oivmK88XcAtbexu+1a9dQoBFP27acnZ2x7FpEhKhGDoUQmEyn+GDzQtu241w9ENYbAYpk11T5anw9pVTIHyMJc45kcZycHPFk1uC9ZTh4L7TtAplOiDkyP5kzrfZJKVl7Jg2z2WxcU9u+G/2QHG0ejjEZ+ajKvF0yrRvadsk/+t1/yI3rH+Lzn/88v/7rv07TTBF8WW+MyFy7Uxs2y47MeWsMar7B2K4bofbC40d3uXHtkPninN/5P36HJ0+ecPfumyzOT1FVzk+P6NuOZeqYL+dUocYFT0pm78/bBf1ZthIqNw85nR/z+PQRr/zWayCmqMFFbj19k9dvv4kAdW3E5P5sAikx78+Y1g2nZ8fc3J8ynU7Z29vj0aOHpJR5+OQhzz3zEVDla1/+Mh969hl+9Vf/TZ574eO4ekLGghrZF2VohlUw4MeHd6PcWccPrMS5KqVncDq3SR4L5BdDO9nN3JvU9O2CSR2ofMVe06BEUwGIoJEi2ZsSgX7ZW15oMukd3jGZTJjHhLaJrusQlbFeh2iNF8f5YkEVMx/+8IepG0dc2sOeZRXfvyp6uj4xrzvUq8hbMINgcAxglKKtY9UflvdqxqaiGkEvu5HDhFTey3qFE7tFB0jGqRlO6/dICXi3cgSHWjd27YmUehzKtWvX+cWf/zl+6Re+gKC88u1v8Prrr5NSYj5f0kwm3Lx5k5/+6Z/hheefZX9vxsnJ4rKG/egwqJawKF/OmeD8KopwCemhWMSVQiZkFRLZokMD4Th8vhBcA8lx9cSfx/xic37cBWP+wngZj97EVd+xTYxeFgEexyiC6OaZ3yqNyiGWnqObix6YhBUcMtZLUvplz/zshMePHtAuFzi1CIpGRV1mOttn0kzJYpEbFaGpPcvFgj5G6OwaQghmmjrHcrnk9PSU5eKc6bX9Me/VvtNtlgh4H+KqiPz665eSEpdO3IPTe9HgGH73rL03vm8pMOtzmJPNNLutRm+c96JCZj1txKK2cTDshPGfkaKpSMUV7dLGaBtjYTKQ2+U71l5fJzm3CZb19q1fy3bfmBjECBlz5nVUkmWb7ABGufoG8ua5h/RZGSKdquQUV/2RS022mDbqsomC5EROEckZSfE9jzYrSkSNoFdHEoePNa5E4VEjnp0TsvQ41yPODHr1oE7wLlMHI017YCYec3BjcdyE4MxxcyJUkshZkeTxvkbEW7oKFhlVTUhS+tyStKcnklyCRkhdi0pGcsAHZ+RxXEKc4F0CeroUmarJ/3Mo0WHsZypRYVfGh28meHF4CaZUKXPMQKxHNSLfEAnBHKBeE+oUV9laiGQCArnHHF5z3t3WGFFVNFkKsrJKP8mysgHMCfJFeaQWsBGMZBHHpDhPcxX6ZArk4Kc4UZAFwSVcpYRJT+qEKgca75EMISnqlK6QRBSnv64n5HxGIptzo0CqNlIU3jPkUldlSw1r48fGlj1mCl6L+saZysM0YiDgpWJI9XEipFJzQUr9AydKHhQp0QgLU98Nc09ZCW2yMGWMlLCblH4qz7sFiWxd3JimJePFov2jnSdG6uh4jkDKw/lXtmNpgo1HHVxNZ9yzUxyBrNnS41TLs2rJs0EdWUw1JyJoTqNqBjXF0yjTShZUSqWW1ZBiZnNsXKX1Zy2yrrVrxggdyYrLRcGsQnLRrnggUAngtgKPOZqqzG2mMBZ2iEH9Y9f+wd5XZZ3AGdaU9TXbOcfBtSlnZ62lArYtXmomzYRXv/cyL738HZbdgv39fbquG0mTqqqM5IiRruuYzWajAuf+gwf0vQUeF+2Sa9eusehasxicY75YEEpGw/7+Pn3fc76Y470fAwtd15FyJhRieFTyxDgqddZJHC+FBM6lClNOY+kKp9AEz3Q6gc6CqPOzE5btfCS1VJU+toDVQD1fzIkxsre3x2QyIdQVy+US8Y6JNMznc3IZr12K5BIcqet6VCsNNuOLL76I955f+7VfM+VmjGNqlQUjCiGU19ZuvRgU/f8rVv1gijl7bVDOGgls5RAyOS9pauHO/Tt860/+hJde+k4JmieuXz8k9j1377zB8ckZomJrb1WZ/6RKG3u7374mo6Suo+s7zvuWvWaPlOB8PqeqvfkTJPZmM6bTKYt5S4yZ2C25sX/IwcEBT9qOvrdzHhzs45ypdB4dPeHo5Ij50QmLNnLv3j32ptfZPzzk6Wc+jKaIhAq0kNQfcHxfJM47KYk3EDjbXbT+9+HBjL29gKTEtHI8+8yHaCrBOyV2Ladn57Rdxk+n1IfXoRJCClTTGd47cooW0cv2RRoTZ2dnpBL97JZLm1jbnjbCImXqZsqzzz7Pc899lNfeeJOz4xNwwQbzFbVn1q/prZy1oWBkeWdc+C93zAep+eZ5rj6+vD6qfS6eb/NgWyA9RaItq+NShiwWa/LOIR6WbUvXnnNw7ZC//Bu/xs/+zJ/jU5/4BL//+7/Ha997hcXpMcvlkqqq8C5wdznn9dvfsxo5H3qaL/zCz/EP/sE/urTdP0qsO6zDpLF+n65yom1SN2NPSgQ1q+Xqq6wTW+9EnVAMvzIZClZc8p0sEtuLyQVlwRWfWT9uG87YwEtJRL1kTIq6LQKHYoSsUjJc7Wh6T+yXHN2/y907b3D/zdu050fUoaEJDX23RJyyf3gNixYr02CR49j1Fp0suaxkRftIpyZhPz874c6dN7jx1C1uffgZYHA0i7H0PidxtnGVemQbl4+Rq1Uqb3f+i0TM5aTf9jku/0y+cNx6u8fIVr54ngu/r8m0xzG/RtasG5NXqXQ2XpdNybeILeCskT3klfJns1bWZlrUpopGN75rvW7P8N56itb67+vvr9fiea+jfqpC7H3huB0xOyt2qxZhc85ST1QTPjiCdzhfUn1cNqdYjOSxiOrE6l9oNh5MTb3gizqlch7NpaB5udScI0GqsaBuXwiwnGNxVm1M5JzsGXemwnHicE4JlY2Trm9xSVC/xLuqFEIPYwpAjhG8JzmHDsSbrFQ61v7yehl7tiYOveXW0gnySMYNWrfBYRJvyhDn3HiN6/f7MpJ8ILjQVcFHsECOQwhq1YTmqSVihFkIDhcqcnSotqgkfIhIHcE5slYIDV4cdd2gMUFy5OTISVnMW9qU6dWiqME3JF2Rqj8Ox+XStUximduNQPAhFNIimHID0EIW4L2tUWK1l1Qt6pY1jbWCRJQcBKcBJ7b2JhJoQnKN0NmgFkvhUu3tHvjajs093ksZH4FV6q4WFdBASliRb5GSwpaNuDaJqCl9rY87nDd7IJfUPl9IvJgxoscJkMx2FcHToM7qgCS1Ira2hlvhUIrNBh4ngRSDRZKx9LChcKiQcD6QsxLUj8SiquJSoVaDL/WySgpeeV+zpetZNZ4AzmrYqFPIFVlBJSHeIVks/UWykZGAalUcf0ADPuRyH52NVbWUiJQjXtsrApIfDKyvDwOxsI3T80gfM9PKNnCZ1IH79+7yv/zPv8mX/+VX2NubcHB9n+VyyXK5HOvgVFXF6ekpIsJiseDk5ITvfOc74CytalJqdU1me1xzjn4odNx1xBg5OTmhaayuV51qq5dzfj6ud6GqOLh+o6iDVm7gcrkEVoFMEcGlFelRBUeoayZ1hROzEbx4DvYm1LOaSV3jEBYnZ9ROcGrXPp/PiXSIZFKuqOqarMpiuaTrOqbT6YrYLPPl2fyc6XRGqhOLs9NxPa2Cta3rlnz1q/+Ks7NTfuVXfoVf/uVfHu+JtX1IPdy0lXcqnAEWoDW7YJgjbO6MfUfdBPq2paoCKbfcu/cGX/7yl3n84CEAKc45PT7FI7xy5w4PHz7g/PycMJlQ7x9yeOMG5/Mz+q4fwgjEHDk9O7MUQSfMDvaYNhUxLcnFRul6u/9VaCwDoM/gA+JgOjmgS5mjoxMmkwl9n1gsFty9e5cXXvgYfd9xOj8lp56nn34a7wPLvuN3/9Fv85U//jL/+q98kb/yV/86fUpImJK6Fh9Wq/e2b/VBIPreHYkj74zAuTTFisxQcySKI8WWz3zyp0jLjlcffgcvjheeM5asa+csfeDx8SnB10zrmr1pg+CpmsD12RSHI0jGc8tkg8dHaEzMNNMvW7xzTKdTTk7POFu29HlBVU8Q4OjoiD//2Z8lVJ5/9bWv49QjLoyLnUVkqovOz7qyQ7VEkMqkoKs86gFR1AowXQIjZIacb19kwTCkVW2me/YSAAAgAElEQVRPOAPBdHX/DwvIasEW5/BFyZNL9DVLyYvWTBahp8dFqJvAF7/463zuc3+Bv/CZz3Dv9df57//u3+b05ITZZMKkEhZdpMsdzWSfWzeus2hbbr/xGjlnPvKRZy4XFfzIIKXfIk4ClAJVqsqya4ldz+J8PjpnOBkXtclkQgi1EYEkC06XrVrEbeazX/YgrxMvg/GfcRsR93fy8Fs0C4tQURzdSwyB7XOuy0Ivw6DEuaoF25+78jx5pfTIMRH7jvtv3ubFP/4Kx4+fQNdRiUNTTzWZMGkOaGb7JDWFUx2mON8QcyZ2ptQIJY/bKyxjJPaR5BwiHY8f3uP+/bt8hs8O4VEriqZDhPP9OqGac7rxiuolY8dtjRuuUOK8ff72ZQTNNjmx/vvbkzjpAgG0HVkcW1eitSMxkrcWvS1S0v6x0cbh+Mv76XJSaaOH1sif4f2rv/viuQb1zEjepEROqz4Yn7FS1BtWNPlV/bv+ufV/7zUGEsdpiaBnh7qIc56qthpfEhSRjHhBKiNsRBQfjJDwXkZJvKi3iL9kxPmxVonVyilGvqtRV2qK6BB9XVi/iBWsHPZMypLILpNdtOdGoiluJeHE/sZidQyKGO83+3K4lyEEVBw59Ruk4HDMOpEzKHFUh3PrmsFvqSBZM6IrwgWpippXizJLxzl64x5f8qiunBL7O2erdaOAK3UkXMw0EVzqUKdo36EofcwkFuCWVGHJfsBUJQhBGhpnhX+zQB8j3TLT9UrfQReVTpXsA3irQxPEoU4JVpn0hzfY3iFGxZKqOVeilHznTceLElQBkqTCoThwlrpofVDIWuesSodmnKtwZCNGZJi7zK5Sn0YyhPKMemfq04yRdC4ImsMa2WWkUi6KZjQZeYSlCSGdpRCWZX99cyxXgoKIpbkJYeXwa7HkPFBSjVQ8vlQOEawWZJDa2i6BpJ2p5hQkl0CjelNrSUDUih3jGiN5dHgOhjXElz7NqHdWw6eQi86B4Iwkckoq9a+spGRR32GkdBAgNORs1+7Uk1JnNlOxczWb0lvIJZ0slD5SsjNlPSlaWmKq38dr+jvHWwX5xDsqKjKZWVWT+p5/+o9/l6997WscPXnE4a1DnMeKFIfAZDJhMpnQti1N09B1HX/6p3/KkydPjHyZhHFnplAKGh+fnFiwwAnLvmO5XFInS5MKIdjIUiUWQhps7mrbdiwPMZDe29ekqviYCd4zqQPX92fM9iYcTvcgtZCVSfDMpnu0vubmNLAfYH5yjPaZg+ke12cHHC/OQYUYO7Rp6MksFlbrZ1JZgW7vPXWoRhsxqxFYqhFfNl5J7RztbJcr8Zai+sYbb/BP/snv8ulPf5qbN28SY4f3FT5A7DeDoh8Ux/zHAashZyqcZlJZRovLxNTx9/67v8OdN17hk5/8JPfuvskrr7xC6nqeeuopjo+PuXfnLrPZjE/8zCc4mS+4d3zE3Xt3qJqayWQPARaLBeI9vvZc2z8kUPHk7IiDg33UZfozs1H2plMUqx0FmbaQm9O9QytwT6byjv3JHtMqcLA3A4k8fvyIqqqsTY+OuH37DrNDU7EdHu5DbPmHv/MPOD8/4z/8j/4T5ssTrh/eYH7ejWvium/1QRknP/LCxqPTwipfNHhP3y748NNPEWPHo0ePqIJHXKapGzT3LJedqUWqgHhP0mhVpAFRIYtJZJHVdtfZKaH2LM4iobFoVdM0zJcdlJ1KpFtwenrMU08/w9NPP81sNqNrc7mHbrXora3KwzVsSvDdxntgDsT6DiZCqT/AumG5/vmSO72dv6kluqO2uA5bA15N4AyFvOy81iZbzCmytlyOUbWcZDu0GLEOPvrcc3zxi1/kxrVr/Nk3v8H927fJXUfjPdMq4KrA4bVE21uO+5ATm6NyvlwwPdwvkaX3BsJAggSyAy9GhMQYWZzPaduWBw8esJwvRhJnkG9ev36dpmmYTmdl+8WVAzY4s0Ok7a3UE6ufJWL3LnGVY3rZcdsTy1upcQYlDlvnH2SN29//Vnctmf1M30f6Zcvj+w+59+ZdFufnRuDUtgDXkz2q2tNMpqRM2WrYl8imx/sK53ozfLOS1ghTjYmWjvn5gsXCUvIi4Mt9kwxDnZ7358RqBvLGKzoUOlhv86ouDHCBdFhhNb7ebdToAmmxRWRsH7v6fSCg1yXHl7RsyxiSEt3eVthc9T3b17Q+nrff22739nWtn288VnXj7yHXf1s9M/w9SMxJtpvVerqsiNiOaIPqo1zWNmmz3efrqoer7/GPEFtzg6W5BZxTXCFHxamlUHhs3SrOvfO5FC0e0nOtoKkbCG48fY5G3jgpBXgd/dr2tVLGfMpx2NWTRDLpsiiM22enkoJcUujU46SogcSIpOAsxWpQ06wUW2tG16C+GY7hrUm0y8i+8Vxb8+uQekxejY/LIu5vfTsuztOas23UEBOu7RAP4hwh29yn9FAK+Ko4UjaHGxXbhU4SSRM5Wc2KlIQYE10X6VMmqm3fjpONdVkGwuE9xqV9sPUMl1dXv68fK6EoZstnnA1dKwesDBHkRCpD2uquZBGL8DpKapxYVqBY4dS8RkDjdEynGuYNT9m+Vod0r1Xbh35V0Y3p/7L5jbJ+bc5b1hzbdVkgeVQojrUgEkjJ6k5ZgMePBGJOSuUmWEqYKVxzppAiK3WSfZ3DuwDiS12mdVLb0iutUVjwpxTTLoXorLaNSOntYW4EKbsBiRuCGM7GaLFBkwrOrWpImkWdS8A0lo1x3o/r+bvDW9klQ7DAiXGWx0enfOMbL/Lk6BF939l7QYhdpmkamqYZPzebzTg9PeXk5IS2aznYPyA7m3+iZgKrujvOWep6KsGIgYSMMZpqaiCa3ap+z+Z8uiK9h2sar89lvIOmDkyailndcH22h9eaoFK2FA+caMW0FmqXaRdLchb2p3tMJxOaqqbCs1i2xHJ9w05YQ4qX7TxobXC6Vp9HIITaxpgavS/FF2uCo+s6vve97/Hw4YOy7bjHD7velZqZF4Li34dt9f91WB0u21ig8p6YFlw/vMEf/dG/4Pbt21w7nJFi5JVXXuHhw4d85qc+za1bt+jbjo8+9yxP3bpJHWrevHuP09NzfBVomgbxnti2Y3kK7z1OPDFHKh+IqYNsKYlDulzTNCxVyUmomylkZT6fM5tMqaqGUKbSSTVhNpvhg5CzqZ5Pz+eQwVcVi3lLqBzHx8c8++xHyYsFX//qH3N6fMJ0OiV3SyOGQnXFvP3+x4+MxBmKHq8cz0GJY7vVkJVr1w+JfUffzbmxF7h79y6TYE7hvGyVt2xb4pMnnLeJ5z++x/7hPoSa5fwc5x0xdWhKNNM99vueeRbSXuT85JTzhRVWunawx+m547ztyElo+3t8/BOf4i/+xS+w7Jf8yYt/RopDVNYcmvX80KEoEzBusdw0tW3lV+SI5gzIKN+3D1pefF3Vo8OwOWkGEGc5ykNEqjjaOcUSObRc8EGuKWvqn2HSFXFlR5FIjHlsN2oC3xStCJoqdKmj2Z+QcmY5P+PDH/kIf+6nP81/8O//u7z5+qu8+Mdf4v7t12mahqevHzA/P8V5xXvh5s2bdDEj3nE2X9C1LX4qTPYakzu/h1CEPicq56mdo4stmjLdsuXN22/w+PFjvv2tb40SVRFhOp2yv7/Pwa0bVL5mNt3jIx99mmvXrnH94BCcRS2swFzZ8rWk5q3f1yG6uzL2MpsbbW5ioNguW+zXF82rnNbLPrftbKy/nxHclvrCnCTG2j6bTo6S4uq1qBa5FSDkTPaO+emCh3fu8PWvfpWjh49tJ6rG4/DgxKLz2eERJnuHiHj6LlPlGorh4Ry4Qqb2fW81sULAOWHRR87Pzzk/PTYjQylkkKJkS7Uokvv34/y6bRC8HUH3VovEtsHxTr//sn/vhMSxnxeVPZdd00CesjZ+xK3SpBQ38snrz0uWzXG8HvlbN6i2v/+q3Z2G9Kz142KMkGxb75w3SZn1cw3pTsNnUkrkrh9fU9WNIpWjsVuFC+e69Lkd6gZd0fYfNewZz7hSs0YIDDqYJJAlWwHYULYjpkekwnkrROlKMsUAZ7kr5f4mqhDGlCABckyoW9VxEskISh08vWZS2bY55kzjrVZIUiuManWShzpmRWniPFUxABtXIc7aNranrING4kekkIiCGYhNqHAhjLu3XOgfVcvVVx3JJCNsrORtFEqVtXJfY7J+K+vA9m5Xlj5ycf6352/1HHnvS80km9y0j6S2p0n2WpaevFySiETfknwHGkkx0y4sHccH8H0kSUuvAY1KjkLfKe0ycnJyRp+FKIrsGcE23FM/EMrv6fxZyIVxV6NhzGHbjKNjIWpwiE9oHmpw5HHeCFCU0jL2q7g1ZZMzcmKoVyRidYVEjAhUlZIGpCCRjMNn8N7GbZaEiKV4JO3xPjBsV2xO+DBHqanzfGMkEb3ttigZJ5a6lHMG9XgfSJFR/WPvqzmheEsdLLFCp46MRySjGsyekBJc1IkFQdc2uHDO6tNpdjb/SoWMgbqhjon1lzhBtSguFHyyKjtDbUBcbeq4nPHBjq2CJ0ULUOWiEB6KczdVU+zRRNaBvK9sVzlvZI9HcaFi4Lcku1IhKyMkcnaoVD8eRvGHhJX9LZfXWQNwniBQe8d8vuDP/uzP+NKXvkQ7t1o4VeXLelWNa06Mkbqumc/nvPzyy7RtSxVMGdGm1lI8T46ZTqeIs0D1QCpOJhObZ0rdUNUy3kSY+NW8VTeNBbpLsfZh3RtIoeF1EeGwsh2ED/ZmHDQ1+5OG6/szDkKgqQPTKqApc7B3Hd+dU6WWHCMH+9eZHt7i1aNTWqeIVx49DnzvZEFMCV8FRC2FK8xmtgtVTGb4AfXeDOcclTQEb1tUHxweoGptPD05Y744wznHiy++yFe+8hUAPvWpnyikbl3GphE5A3YEzjpKlAUYSnyEYCRrVTXcu3eH3/zN3+TZjz7D+ckDfvd3f5faB37yk5+i6zpefum7tMvI9cNreCoe3H/E0ckJUgsfeupD5Gw+c192mwzBUdVTYuo5PjuztGCsbo4UX/rs9Jyzk1M+8pFniTFzfHxMu+w5mE05m3cc1J55XJK7iLuVODs756MffYanP/Q0ITgOuyWvfPdVxNVMpjVttyD2LS+/8m3A8VM//TP8j//D3+Ov/LW/ytO3nqNuGro49IPbIrlLL72Px8y7I3HUiJm3SqkaUqnWU6rsQXJrP0tObdlmTlNv2572tgOFqwN7YcZH6glRPW1MtH3Lw/v3eHx0hFQVt249Td8uWczPrKr5/Jzcd6BWQOl8aQqMxfyceduSSgTSitkGnjy+zwsf/wR/6Vf+NZ5+6sO8+cZ9Xnr5z2jnPYuuJYQJVQ2xz8TYmjzPeyaTGucCOduil5MgjU16KZpxak6+oN7jSnu0RDOHgTAUMDPixhFCKbrYF2Ok7EKRouK84Ip8Ghh3ufBFtptKbQHVTFX5cTJOKtQCoalRJ6ScmU2mVnAvCb/487/A3/j3/hpP3brJn379X/Evv/JH5BjZrwNE2zUkVErbLdGqgmTOzXzZkvHgHIuTluPJGYcHN7l0W80fEcyP9PQ5kVOPZIGUWZyf871XXuLum3e4e+cOZ2dnqMBkMkHEl3s4GReqT3zieW7cuMFHnnue559/nkmzZwU7oxmQITiGHWfW1Qarh9qcJd7iIRfVcZp8K2d+O0rp2NymfJDnD8hbk834c/jc1jNo9RyuqMHjSq0Mta1XrVqDJ7tASnPeeP02r738Mo8ePKTxAjFyenbCdP/AFvswoZpMqPb2EV+bcqdPOFIJvgs4G8ch1KTYQyleLqrUQaiGdIfcQSmMaH3gEdIqOurXPLr3AVRXpMI6qebWiIqcM86n8RjnbAtRHYpzlALSdr711KYVaTU6ERdImULC6OrfkBqE9mMjN8idLQzO6ADnypa/qwD5+LuIjFuGDzVCQiHftROit51/0lpbfRYjN1IeSaC4do6x77bSrax46eq1YW7zak71elqTLo2IiQOJoxa5H8mbst1w7tNYLDHHaPdO81DBb+w/EQuTq1h9CenWiKbxnKv5YFSEIqWgb1E+vO0I+iFDIUhVisAKPiidClVToV1HzA5f7eHE1uPae6pQ1CypqFREiRIhQw4Zrx7waPK4Phtp40rtGJfJnBeHoaiWyFR1zfnZCcFPCL4B39K3CyjEUUxLXF2RpceHCic1TjyVD0y8KUazn+KkRqhKCmwhpqRcaNkBKPgaxIIhOE8INa7UUhG/KjQvImtb1+ZR+SCqZJIVaa7DOG7EOZZdNxZCVmEMxqyn8nVFpTCobgXQbJsmSPBoyqQ2In1E2o58usAvWpo+E/tE5S0tLESPZvA+I3Wi7yPaBc4aj3eOia9wvgHnyW2L4FjMM/3SEzu1+jhLS1kSD51rCcET9jI5dzT91Wm2P0pYXxnBoAqDUsCmIlN+OQk4anJRBdqSUWrPJHuYhvSb1Rq8UtrZVGTb0QIQrB6gK7bnUEzaUWOh2oyUWjfOBXIJ1oVCgNg8abUdV2umPR+ak41jLUq+kgZFKQLsXLHTsN22TAlh6Vq+1JoTaZBsO1E5sYLTKWNO53AJfiCEKWlQtpOpuGQKGafYTl/raiA3zkvem3oul8Lt3leWUpfLDnROoOwE5r0HXdnpUuaEnALq7TqtmHkpJp0txUu1lF72FcMudZKkZES7oq7wiEZc8kRvN9fnVd3IDyJUIKkpoTJaNsBYU2gK+NbSAGUifOu73+YPv/wv+O7tV8iVp5pOWS4USBxe22c6m9D3PUE8J2envPbaa9x/eM+2RFbblWymQuM8WTxx0RI1081b8yUUaueZ+imyZ3PlbDZjSIeb7k1MGVHWwShlt7CUsYwGqCpTvKkmJpOaqqp4pp7gvdCEymr7TGpuHNQ03lF5xyQYYXktLJFpjWrFokssz06omo5P35zysVsv4Jua775Sked3OOvOmatw3ndoPeE8JbxmqrykShFEmR+3Rh46ITmPrwJ79TWOnxwR2w7vAg1WrH9+/JDb3/02H75+yJ/7qZ9Ek23Sot4USlVeEe7D/P1+dcrfK6yCYGVbdjEVf9ZEJYFvfvNF/uHv/Dbzs3N+/w/+KV4TH7r1FCFUPHjwgAf37iPquPnULZJm7ty7y4MHj/BVjaaWo+NTS+FLmewc02ZKsz/lzpt3IWf29mecHp/RBGU6mdF1EadwcHBA27Ysz+d4X3H94JDz0LKYz5nO9ll0Vut2UnuWMXFtOgXgpZdeAnXcuHnA9Ws30UPH0dETCyKqBev29qacHh3zcvw2f+/v/F3+q//ib/Fk2aJhimClzQb1YM553J14fcON99u4+b6UODIYJ+9iBh7VArlsZ5ptd5q9uqKua5qwtkVfUqImkwlrwnmTTeWc6RcL2jNjX3PsWS7O0ZiIsTOjVK0YYdQM2bYE75YtKtBMD4gl6pP6JTkmDg+v8cILL+Bc4MHDe5zKMa5yLBcduWzfuLe3h/eeth2KVOZShDCUyTqjKrgKctxWSaSRdMH7EhVaRWmNCNJSm25QLBU5aspm0GS3Uel9PH/Jo3aOURYMjhhNiu3dxLZR87Y9dN91TCcHTEPF9evX+bW/9EU+9txzPHl0j1de/i6x7QmixL61qHbTUE/3UJb0KLkoj5rphOXpGQAxdpydn9N18b01Dsvl2sMG5EzKmdPzcxYnZ8zncwBqJ1B5DpqG8z7RFgdvUNbcvXuXk5MTGy/AjRs3eObDH6XrIqnPNK4quzetIv+2GJRaO+RCMoCKu7Ar1HD8u1VVABsEzqXIeuUxg2risvNe9X1WvyGB5rIdayaLjZ3z0xPOjk+IbWfbDLvMdG8fXCBlpRLbyWeogZPzYGirRSWHvPicQRLiBkLDag24Qd6LL2lfZWcBocTvYNiV5P2Kt1K7bI6BNaXNO7yctxo/bze2LlXovMWx72S8bqhqnIykiXMO4sA6yRq/dDG6oWvP8GVrydh369eRM5ozKW8qYobf1/8BhdxeEee5fP5Cn6ylWFntiuEagXEBZzzPOlm33d4fN2Rd9VR+BqkQcWX3PsV52/bXIwRfauAMJE7BVZGooSi880Mak5Lj4PTa2pg102fwlSAucTY/QSi7TqgWxYMaaVZ2+hm22l4pNvxIjrmy6+BY0HiL2BvaOJIr7vIaOpcpp0ytVBxOrNadkzx05OqcsrYb1dY5nEIq1yWsKcrEUtVkCIBF222FmKC3dCrp01inRGLGFwI/JQUVcnZ0qlQOcuVI3nYlspIx1vcxKl2MpGQSdBUppYUcLg8aDUtDfzd22w8Dltaz2uFJxJN6JYRhQ4DhGTZSdCCcnQugjpwTVe1KNs9asfWxj20dFrUo6mq+EKwTPCKDjWTEXBrI5EFVhalbdFy/B9Wn7SYWxBwBYLWjiaZxnTNHqBAsMjhGFNLJ0lvM5ig72CGo9liSVwl+yZB8bztSDfURbcvwwfG0YKS1tTfSaFTcaDEaEs5ZUFHESPbh2chDup7HajqWLdGHMZEvECsO58t8J87UUM6KaRN6SAE04crzayrmtaK4AlK2bxcRUpVwcb3u4Pt3PX87bD9H63PMSOrroMCvuHPnDq+//jrn5+fs7U3G/vLec3h4ONqkzjmOjo548OABKSlVtRYcdWKkBha8jSniKnCppJEWBVlTFb+qaaxYugh7e1Nmk2kJ2DlIgZxtBywwVZorW4arKpOpkThPT/YQEerg2Z80VCFwONmj8eYDTYLNzXXxYTQLSTqyg5gjh3t7HE4bCJ5Hhwdcmz4mtkv6lKk89JrJfTaStgQ+yJk2tvgQwDuCWznQfd+TYqQKwYqGZ8uiOD6z1LPlcokPNVJViPMrQ2OHDVSAuMSij2aHi8PXgb6LTGvPN1/8Bkf3H3Lve6/z0Wee5s17r7E/3eP+nXucHR8zrWpSUg4PD1nGxPHinD4ofVYke0StnpgLAYeNr+X5nNl0Yrscn5/RVA6fA9pnps0EEUeXE32K3Di8wdHZOcllbty6QfQdOSzZ29+nX7ZkVZauhWXmMO6zN7XXTx6ckL3igme6V3F22uOkIVQNGgOz6QxP4PTkiO++9C0+/unPcnI+x7maUDlSmbM0WYaBYBkGZClq0PT2vhnv3cz2rgsbDxOXh41I/5A+dRVWAV2l8jXUjmlTUXlH5RyaErFbkHPA+5qu73lyOkd9oJlOuHVwnVzSkhqtWJye4oNVP2+LJD6p0i7m9H0ca+B02SZJAWNmc8Y7x+nxGXffvM0n936Cw/0ZH//Es+ztTcY81a9//UWOjo54/PiI/YM90EDfJ8tb9xXeV3TdEhHPcjknNA2z2SGLxcImyKzF0RdgkCsmEqUGjgd1aqlaLpfjVrUlYq+IU6pq2H5zZUSv78BhObdWgX1T6m3FA3uxsru5T0ynU37+85/lsz/zGT7+8Y/zqY89zz/+nd/m9de/x+nRIyT2RLVdHsTBolswPbjO3uHTLDrh6OiIZddzePMGMScW8xZX1ThXcf/hg1Ep8V5AsUUziMNVtvPBYnHOa6++zNHxYzQmtG9JKVI5IaWIiz1BM6EKiMB8fs6r333C/rVDVJX7b97hYP8aP/+Lv8Szzz9HLEUPLxr9CZEKMKNZU7bE9rcwjuUK43k7deYy59DMOiNAtx8xUcZc0/HZVCie1Kq/pBjIl3yn7ZShxWDuxzY4PPQdJ48f8+DOmzx6eJecepI4SIqrAn2KpCxMJ1OqMCPGRCzij6aubXeMslvKsGOP5W2DDts+ihA81JUgLpJjwlUVYIt6Fismadvxvh8X5BVTDytSYp2933YoryIuLj371tgYiYYNsvjt06mG/PyrlDjrP4co9FXHDe9dpiwTGeogWNTdHJfNazC40VHmkvfH+U5Xr+e0IsUvI3G2Pz+kQ8Ia+VLWhnXiR7cIoJHEwcgGM8pX9R3W06nWlUSjOunHjHGMlLYEb6rP/YnNfS54+tzjsKLBlbc5IJVbkzWZ2gBIyRxxOy/ouINVSenQSKdtuf5S2FgSmTN8dYPzxSmz60rqA8vzbNLqUrC4T52lMZWtjqXUzyIHnFSm4gkNdbW3KlAsq5ol6wb68L6vAi4ExLmxfsJV8+ywVg8kPGJFbkNRbwwp06a29OOzPKy1Y+BJLa0sl3ZJLs+bK2qIZGkC0kZY9rguQRfJXQfzWMguxyR5MmbIpuzxBGKsqUMpLh29OdPiqL1DncfRY4luWggGbwZnL9RpgostTstW0QG4sIr8aGHbYNu252Bqmrr2ZX7QolwxokVzHMk7AFeuyO53UTOXmm+rOU7Yb6ZUbsJZfwayMLJfE0hCpR/bYc+F3bPVNvNmLw1b7JYBPAZr1m0vG+ODomhow6DIGhSiUs4Vxs9njLB0JS9Qs9mkiPy/7L1pjGbZed/3O8vd3rWquqp6umcnZ4YUyRlRu2JRtiJTYuRYRhwFSIAkCIwggL4kAZIgQODASKIvypcARoTEThwbMLIAQSRLMiwBkgXTlEyRlERRlLgOZ+np6b1rfZe7nSUfnnPfWqZJzUjRcCzkAM1hV1XXe9/7nnvOc/7Pf0ElZpAwcs78EOXGGbQOyAcXxKAuQXLC7IwCxHiPtSqxfyDEFq2rjdw++IEBmt5LUEQVxc9H+EHpys/k/hgBbAfwSAdF1JqIwZgA0RK0AEghDGbOIk80xgx42wYkCgE0GmwkhIh/T+7lb3+cr8OHv5//+iDl7LqW09MTfuu3PsXv/d7v0LYt0+mYtm1RyjAZzwCRnXgvCbsbGVVi1mutBZQxFk+k7zoCEVvkbG1tAeDqFmtlXZhOp5RlSW4zRmVOmReMRxXTcoSxiiovqNIe6L2AwlmWMRoNLHW1ifSeRmmwb21tMSpLetcSXU+eCYOzKHIybajdWoyrgb7ztJ0kYunlkpgZTFbwHc88yeq45k3tuX18wjjLWRNZ9aJiQGlaxAMsOidsSi1TllcAACAASURBVM7W7WE9VlbWZrFgUtRtwyuvvMK1a9dYLpfMt3aIUZQDVllC++6dTd7bYwB95UygoxbPyiLfSJ6KouBffPpT/Oo/+SWOD4/4juee49atm1y5coXT01MODw/x3rOzs8tkNGa1WrHuerGiCB7nAtbmrJsGo1SSVYaNVYUnErTssUHJ2p7nudReWlQTWmva4DCJTdn3LZNqQuNW1IulkCrqlnI8oXUti1XN3nyCGY9o6hqrNUEbTo8fkmdiFr5arShnYw4Pj9Fac/XqHl/4wueZ7l5ne/cxvHe0fY/Sli4pL1SUepaNx+XbaK6/y+MdgTjDtQ+LlYthU2Br1FlRxMBWOCue4NzhJkpR7/oWn+UQHDoz+Bix2jKbbhFjJC9aVl2Dj4p2XTOeZWRVyXg2p3eB3gtV0XvPyckJ3nWMyoK2qPHeoUKkXdfks4yu7Wn7jqKoqGYT+rbm3t1bPPfc+5nPx5ha9KR1XdP3Pd/zvR+lrmvu37/ParVicbqi7VZMJrtoHXn48IgQIn3fSlFnQCu/6VJOJhPqusZ6iyrUJvoPHDEGiqJMCD1kWU5d1+fYC4os15sio23bzSJmTIZQeM8Ogpvi1ZjkAZM2EG2Y711hOp6wt73Ds09e59/4yb/KM088wenxQ37p5/8Rn//Mp7FWM52NmcznOOUZlyUqy2hcy2q9post89kufd9zcHrMarVmPp3ho8LYkvl0ysHR0bsKeA9NNA9kVmFMxumx497d29y5cyfJJJyYBvqexbJLSaAWlTusMmxPx/TO0ncNb7z6Kk8//QwH3QN+7Vf+CR//8U+ws7/HaGTRaIILqRMni2CWDTG3IneJQPTugmzqnXQ8zx+ihz8BUPGMQg6b+vLC74/q7IAqBWcYYJ9HjuHnNq9JTItUwLtu0xGnb2nrJbde/ga333gD17SCui9WdMGR5xU7W1soo9G2BDTrVSMHscxitKbvHcF7et8lL5yWrmtwrkWFHlHKR6LK6fuetm1p25ZxVhANG4GCYFXf/sPxo4YAFRcNTy+zBS4zcTZAztv6/Y9isVwEKx4FzgwgznmGybfyabkga4ri2/DNrmWQJsmB4Ow9XgZyL1/b+de3ycdEhWH+saGjX/jjz95n8F6YNPoMxDkPUp03LR6u6VEsjMtA2qOArRjPZFPyc2f//rInznsNxDk/tNZoI7KR3A4ABEQ7sBACNgHBXbr/zokXHCQ208YcV+ETi06YAwIfDGnMYpYjbL6qeJYndj5B39d8/fX/B+eWxGhSsR7RVuOCxybD1T46sBGsHDwF0MmxphQzw9TxDYlhK9d2BtJtmDo2xTlrqTX0Iz7bjcwxpTbGmJhB6W0weOxE6b0ZY4mcMXHOD+k+B7xz+C4h2CFKI0ErkaVEL39vOnzdEtse1fTEtqNrHDqASgcWpRR50LhggBx6R+iEZdLpSJ4rdGbACStCe0NQLkloRLbp+gi9QgVD7AK6EMac0vFdJT8oBVoV8ppxACSGySLm2APz+IyBFYnxnGF1kqqhHD4O+5Xcp8LmjEYl61PwquKKfYrMGHTWcmreoA8NBSWZLjio79PHngKP0RbnPVnyuYJLdHl1fj0VxvPms45yfVqpBEyleO1kDCpLit38PFoJKHO+RtbS4DNaJFYhsGGZRcRTZ2D4aJXguShgndwvUMO9VAptJUZabE8iOmTE6FDkMieUIXpZw0IQplYMbiM1G543reLmWvq+PzOXDQqs27x3pXUCyIVZa2KUxDklLCNDYpijICqiBhMiAQFstY7gvmXf6z0/htobLu7H5w17p0WFpuCPvvQFPveZT3P39psU1hB6R+gDjz0mnozrtma5XJJlGbdu3aJpmk1doZRKjBqb1qWI0TlWKYoqx7mOTBvGo5LdrS3KsmQ8nsh8cmKAbEIkc56xMZRZwfZsyv7IkGUZ8/lcAJvMJrsIAYxsLsB/GTOMUpRlvrEX6F2L+DLJmhJjJGq/Aa/bdUtTd/T9jOz+AxZ1DbGnrAr+wosf5Pipa7x86w6HqxU3Hj7koNas2kjtPW0QECdnCB2A3gsLbjQaYSYTovP0XYdrJAgjhMhXvvIVmqbhpZc+ynd88MM88cwzEGJKof3/h4yLsv+IPOu+6wnRUY0Kjo4O+b/+j/+dqip55rte5M1XX8VaTdf2dLGjGk9R0aOtpRhPaJce39Y0fcd63TGZTAhang1Jpyppmoa+7+iD37Bbg9UUWU5uLBiDS2bXRZmjlOLk6JiqqsiwtIuavf0tQjS4PlCVJSfukK6tGZcT2nrF2lpG0wllVfH6zTcoqpKyqFjWa/Z395jNZjx48ADlI1s7c27dusWrr7zCCx9+k4OjY55537NoHckKg+8FW9BYoo+JHfneBJ3fsZzqAkNgYJWeK2ou05Uf1XEWnZyj7zyq8mgksaqwBWWWszWbEbQiZitYSqFnVFq8IhQ2wxqF6vpN9Kh4IbQYBQsMdbOEPpJpw2q1wgeH1YaqKMSPx8v1913D1vYWXkN/fIL3PV3XMJmMmE6nzOdzDg4OWCwWlGVJUYhu9fj4mKIo6HuT0N6kgU6ysKI0uF7jdcCaTMzILtyrRAHemBIHSdxEvDLEY8KlRTymjS8V3xGc83gfN5vs0BkMwTGfbzMZjRjPZ1x78ikm44r3PfUkzzz5BE899STL4yNe/cYr3Lv1JtPpWP49YlAcU+Erm7OkmvS9Y9nWRGPJbIHNLdYUWFujjWh/m6Z5p1PpTz10YnBJJ10AtL5paJqa0HcURUGen6VMWWUTYyxgjWUym+J7x/HpEYtlzXq9Fvld13Hjxmt0XcfVa9fIsowsK/Bx+PzO0YUHXwQefRB8p9T182COHCPCIxunm8Pn+f8999y9s9e9CDYoJQkonQ/U6zUnBw/xbYdWAYWhcS0oGM+mVKMJ3itJpwkid1JilyHSRd/jNofwM02y9x57/jqDJzgxOZdnJRmMvTfXzX+px6Pm6Z/k315e4/+433sZjHqU3OVRB8xHgVjnwZvzwMplEOf861zeiy6zj/648c2Anm/1/W/POAOCN0b6UUsE88AiIaKsRVxZSR1U8WkbZKJCkLp0HzeHXGEiyJcS0JyYOfLvAnt7z7M6mvLE488xnt/l1de+yIMHd4F0hD8HWL8FiEweUZuf4SxJ5TLQff76NmDApaTER9Uhm9dmWDvPs8wusbei+P+cl0Gf98SJl+deiLiuE0+lqNHBEzsxc4/uDGwcgEfvxY8nECQRUAQ+cg+iwveOiDSLogaMQhkxnd6YBhuR3yQ+BS4mJnDgAhPu3R3nn7EzFgsYlIkbYuXZZ+O5nPaoMPjQSOdYg44C/mRZRllMWR122K4gNyNUrsjDDBc7row/wmimcKHl3sFX2Cn3UFZxtDhiVm2zrJf40KTXO5/Kozef43Ddw7gA7MSLgQdq42vmOAugkM9o+FdRJTlW3Ci50utI6MeZd9AAMguzSpGYM8T0c4P1eEAbB9FizNA40ISYfLnUAAiZZBot5smDo/KFVo8KKCxZppMJaToeRI0yYlAelUcFSwh9aiiKTTNGwDadErEi0kFXiWQZYkQFLYCSHuRV75X18k82LjTSYrywHgzfX60WeO958OA+h4eHtG1LVVWp5mdT+yulNiyT1WolB+tzDZEYo4SqJCCbTeNQUu5sDtVoxPZszqis5IwToceL/2CAydYWs1HFuBpxZWvOvIoSYLJ3haIS9kNWCIhTFAUmgTh6U9rLvDFKk2nxDQNROHjfg0nPZhBg0ipNCHliFGV0vXjW2VnBuLB03jE6Lqi7lsZ5nLP0UeNI9hRJ9hXTJDrftAG5d23ysMusMEkODg547bXX2Nne5erjj5Mb+45r8D/vY7AqCAFh1VnwXlPYgr7vuHnjNQ4OHjAuMw4ODjYeQuPxiNVyKSEDKidPybTGypzxLhK8ND36vt9I/0KQsAkfBOSV5Ds2suegkyfPkF6lLc6kNOmQPCFDxPeeXBtG5QhjFE1RsFr0hFzOHeu+xbqCIsvRVggNRZZvWDjXru5hipxXv/pVZmFO27a8eftNFosFO8VETN+VEmsWndbsCAPz8R3f5zTt/qyXuXcM4uhzi9YwHlmMf5OvnxVpHqPUJkGkbxraICkAbqCdeqhyQaDrrmVxeEybr0WHOZpgjErULc2oKjjtejF7tZEr27u0bcvi6JjO9QmkGGGtpq1bvBK0+fbt2+w+fo0rO3NWyxPGk5LZfIwKkFUlV/f2OTw8xbkOazV13bNcLnn55ScgavLCYk1O23bUbY3ONGVumU52OHjwkPV6zdHRSZJmySLed566rpnP5wmwsYnSGEVmEiPGRHz6/0VR0LY9OnU/JN5bJa2seOJcvbrHdDplOp3y4ksfZnd3l6v717iyOydGxeNX95hPZ7zx2sv88s//PHdu32Jc5GzvbrM8PWaxPJXEkiLn8PQEZSy7u7tUoxHee46Pj8mzkvl8TlFVPLh/QLdquXJ1m6ooOT48eqdT6U89NkkqMVDXK+pmiXOOsjA4ZeXQ4gNoGI9GVNWIvu9ZrVb0fcS5gvFokjauA+7dvUVeVOzt7fNHX/xDXn75ZZ577gWuPf44Tz/7zCb1SzpQnqFADWkjGdI6/zSd+AsgqXzlrAs4FLwRkUpp9ZYi+O0W6Uqp5KmTCtD0bzN9lrp1//5d7tx8g/s3XydXkabtuHN4gMIymU1wSiR2XlmKfAQ4lJekFuUCTdeIhDEK4Nj5LtEtHd73qOAp81SUxIhzLU0rbBx5/+ffuyIktsZ7jekg985fkE/FeJFqHULYHFzl8oWxJ0W1SuuqOlfQR0DAVHWhKyxjSEvZRGSncZll4i8lNCmlLsiTSNd0ntly9h78hX+XjEMSKGClSxEUQa9RQbrlRkMdrXRqpNYkBI0u5B7U65auFUBbuxZjLIXNyKzF6Iy6XW52PzGytfjgWC9P0YiuXylYN2sybTFKE0PE9w6vAkEFAmJEfrmZMAyTJAsb2VREusVhOP6eMYLwKc5UKfzGu+PsHlulE2MgMQQSZfjbCehEAl6BVZIep6NFuVYSR4LBKIW1GTYYUJIcFSOgAhYnEgstczjGiBkMtwkEHBaV5GZASh0xKpdIcRxZLt4wJ6s7fOPLin/6zx/g3Zjv/9hP8uEXF3z68/8942JPQAbGtL6lyDI61+GdQdOi7ZhpXqBNhs0KsiJHZTIvDaCHORnEQF0ZTVQGF8B4iCkRTyVTz8u1yHkgxbmA9wEX2s33IbHpnEKlkIHN3FcusSHEGzAqTVA9Nkb6Zo3zHf0m6TJijfA1VOtwfU1wPa5raIOjVYFV6Fi3PdbbTcpVVArV9+B6LJIYpFAEFYmZEhq81mhrKHJDFQIx66nXDqcC5FoYaw5CbujrHpMbYn7GKnlXhgJji9Sk0sl3TeTf5y9jWGOU0im1TAG9AAQEqryi8w4VezARrzyFylk9CMwmYxbZMQfNHa5VT0NXcXxzzStfucX2zj57sxFvvjkljj0VOZP95+imGSfuG6z6Fdee3kPlyX+QCAPTJYGYssfKmhxxKBXEV2RjRJ8OH8akuWOJIWJMhrLCplEIm0ULYpjeqwEcYqp8DhQkMMQin8lFh/lnxIjdeEIMYngbIkr3EJWYaEfQxoOX1cxHBTGgoxFmmFd4L+vgWd0wsHItYmpb0nvHul9RZJbK7jIuK7quofErgp+w6u6B6QkJ8PE+ovCYKCBqzILYGiDsL2XF/0feq8zx96Y8+q3jMqPzcr01/N17v0mF8t4zyjSLZsVvfuqTHB8e4F1HUeQYrZmOJ5R5ge8DnZOI7YODA5bLJd4Hqqq8WD/ESNN0ON9TVKXUHJ1mb2eHrdmE/dmcSWYwePCeKi8ox1Nm1Zg8s7z/yae5trdLlRfMpzPsWECj0WRMlmXYPBfpi1IbQ2EANRpATY8y8p571ycmmPgoeW8wvpN6PEQMhtx6dBT2zHLd0Dmp7Xwva/JsWnGybNiaT9m+c58HiyWv3H3I/cWSGETehRrS585qPq1lDvV9T1AQXIQozKZ21PLqq6+ys73L7v4+V/auMplN37L+f7uSI98b4+xexhjpO7HuGI0LfuHnf4FP/rPfoCwszXpFowJFYdnZ2eHw+Ii6rkEpcmsZj+f0fcvx4pjjoyXRaqazCav1Gp3noBVN09DpDpek2XpoMGmFDjLHatfRrmtJWfORtZXme2EzVstTFBlVUdAuVkzmc67uX+Xk8Ihnn3iGxWLBarXi6tWrrNdrFquaOLPsP3adk5MT7h0cMp1OWZ4eo1RkZ2uLj7z0Il/8gy+ws7NDbjN+6Rd/ge/+nu9je2fKfGuLrovYSmRmLkSCMuKNc2mt+uMal+/W0vYnjhiX7tjbb5Rf6ISlN57nOVkhqU+uX3HatDR9R5ZXZFVJ08bNQb1erjg6OSXLc0yey6ahM1brWg7UyrFYrHC+I7iecVWSBWHA5DbDFCUk6qNsbMmbo3e0bZvMuaSrWFUFRVExnk7Y2d3Bak3nHHle4pyjrlvyXBDm0WjEeDIj+kDbdxgr1MeiGnP08IB6veTunQccHx9z+/ZtHj485Pj4mJOTE6bTKUqLtjMvLApDR0MMmiwrgHZTgIbe4bV0C/PcJlaQFEfj8XgD3Fy/fp0PfOADzCdTsjIjt4bgeonoa2tef/1V7t2/K7rcKqdp1yzrNUFB5x3NUpI4qrJiNJ7SdR0heEbjMSpqRpMxbZ9ieYlMx2Pm06mYVX8bhpT0sik453DRkVsxI7YafO8IQT7rzvd419M0DR6hr1679rg8jD5QFSNslom0DU3TNNy/fx+b5+zu7TDf2jnrAIeUULJ5kIV+MoAhl/XSj2TpPIKi9y1ZCpd/RUzP4Zlc88LCEs/VSBfA1VSQumRselZAp6sJEec6Dg8POXh4j2ZRE3xPXdd475nOpuR5TtO1GF1sktUISswlo8htnHM410tkrvMbAPIyqLDp8OuLzKbhXiolXcj3KivnWx2KHsUAeCdDPs+LIN2fFiB4C+uBzZL8yJ+7fD3D9wZJp3QTvbDyQoTUdWl9JDOGa/sTRpXIFtbLFSenC5QydK5F2FmOvo90sWNWZPQpNWjZNSyPa+ajiqIao0JkvW5xdUsxGdG1Lb1PclakgI7+zLhYfRPjb3kLcfMnxuTxkjwzREpxlhAjXc9koH7pvlwGFt8rXb/z4FWMMck8DJJyaDZMAaF1CLNGfEvS35Mxa0zPsjBWzh9iQJgVqQOtxF8HJfR6FSK5XlLljoP7r/PC83+Jr/3BLfKPfpCnnv4IBwf30d5h6gKnVwLeRL3x8orh3KFJTr4MspvzBb1LTRAXpPNnRdd8jvFw5sd0kb11ZnQ9MGGGeTN0wJVSm4Sv8/dVMbAlZO2VZEREE+M8uJjkvAYfHdFFIgqTDh596kj7GAiAQxFRBB8wCTzsY8B1HcE5dAy4ZGaqtKbtHDbXFPkIlejoeQh41WDLnNiuCYDNkwm069FWGBze9xfez5/9SOmC6hxgHC/tiVo67ZphwwoXKmClpbOb55JUKibXnu5oytZ0m4nZZrp8ln7dc+/LC75+7w2K3PFdLz7Jlcf2maiM9z+xy3K0wNiSk9cPaRv4ie/99/jNP/hVvvb7n+Uj/8r7abykKeqQgzpj2IQNXJPAtcSiUfFs7xR/L4+K5+V2gYh4u4E0nYYhc3hgfwloNezdEkWevp5+t9YRHxIYqfXmeyqAMok5pw2QoZO8S5kMH4URk4SryWPuHDtKq+Sdl1gfxjPOJ1hrMb3i2d0XoDfMmXPn8Dbjcs6D9Q2O+wdUoxFNu2ZUTlk3R5J6pKx485D8oQzipRPF4NxvGN7xfOrze36cr8s27MZLY5BRXf75k9Mjbtx4jeXqNDVtDaNRJklRqdGitaauhQ3ufcBaw8X9eWjYSLOwbVusgmo2ZVTmVFmO1cL40wpGVUlVFWxPJuzu7FDmBXtX5uzuzCjzgq3ZHF9JDZuXxUZyqzNJyRso/1EhvmrBE4MWNlgKo4jpeY1Rgw4i3UyFm1ZeQDqt0MpSjioyn5OXBb6VNXCXyKhoWNQ1i6bHGMvBYs26aWmRBDY/7OWD/DU1Xi7YB+jkmZXYSs65TSOlrmvxWEly4mG8Hebwn/chqgSN0n3ydHN8+Yt/yM2bb5BbIDhGoxGL0yO22Ob4+JjgIqPRCG0NITg61+JaRx88mTKYLMMvVxRG1s8u+S4Ndb61VvZlo8WMOylIatOcq1nETyzLMvquQ6XzVJUXZFphUZRFwWw2w1ox8hbZskGHQNd1GGXI8oIQIovFAmLk+PiUGCMvPPcse3t7tG1N06w5efiQL2jNS9/1UabTETYrLqVwp4Q5NRjOf5s+sG8y/sQgDrz1QXjUwnbhUBqjOD9rTZ6NGI8rrHI4F+jXDb7rsV1LVc2Z6Yqt2Yz1eslyseD46IAiL4kEDh/IBkJepgPEwMixNO0aQuD09BStjEw4hKrYdR3aWDIrB4Xge9bLFV/+8pfZfWyXp556ggf3j3C+I8+nVEVO33bMd7ZQaJzvCV4xm8He3hUODw9R6TWs0tiqYDweo5SibVueeuI6Op7RIlerNbdv3+XGjdf47Gc/y87OLqvVildffVU0qNaiG43Skb3dPe7ff0jb1cSoKcqM6XybF7/zI/zA930/V69epSxLWcwzzfPPPy9dYSsx6DFGmtWa6Bx5lnHvzk2+/Edf4lOf+ucc3LuLCRrXLXFpsSvLEmMtVimmI5FYLU6WlKOK8XiKzjPatmW5XFLXTSqqLJPJBKLi5OHhZQb7n/nw3pMbnSjA0v1o65UwZXxP3fZYYzbG1kcPDzapVVmW0fQdX/ziF9DWMJ9uoa0hBk01HjEej1nVLeHGDY5OFigd+fAHP0w5GScdtAAhIS0wAYXyZ4fGyxvEI8EZ3t6B/JEbjj5XLKhHHMzDefAgdT4vUQLtUExvrk+hlCQerJZLXnnlFR68eZOwXnBaN2hruP7445yulpzWC3ZGE6IS2qJGKI1ixZmliHONsgqcMCf61tP3ndBvnUMrj1K5YKsKTD5iMhFm1AAQey9mjCKbTOaO78Fx+fN5lNHx2z08XQbwzseUf6v58qjvq4GKH4MALPJL3+qPYy4ekAcJzgZAUkMHZaD6S0StUrD0cti1CkyueWoeqYqS2TinMFBoR+cDyjvMvEQ9PccFJVHJALYgRGg6R7z3DVoPeVmhqgofI8su4wtfepnlqqXMR2gVaJcLlFLkRQFKirUBwFHJqFbJgzG85bN7mjrpCulASZEAZ1KgdJBkeK7k3/lw8XMhvcZ5BpO1VgyBE3igeXu+R38WQ66J5JWRoTEYZTHKyLF046t25hmgjMhdQpBnL8aITzIPOU/K4VLYAnIPtZEDrNCso3xPBRZHh/z1f/0/44d+8Lv53/7+/8kP/sC/yv/4P/0Mf+0n/iZ7T93kzcP/GZO/RnQzotdoVaJjiQoiNejantyIpNiHntyUZ4VgAl5sXiRjYrNZ622WXZA26ATKeX8u2WgDXiPvJ3r6tiWEsGECGmPIsxFqMKxVKjEtDQSP8ohXmvP4bk1sGlTXQt+Db9EhFbqhJ6AojMa5jt611F1N41p67+iUk8Q/2HwmVVkynmwTnKddrVnH5AVU5phJhp1X2L1tbJ5RZQXKOWxds6Zi0d9EB0eZG4zSFIVGlxqvI877Daj17gwJbkCfSYRkG1IbjzOdGG5on+S4w1559uwG3RA7zWiUo7sC/+A6s6Li1u/fYXe35ytf+hLb821+9C9/grIyjJTlc5/7HV6Y7BGV5t7iazzWzwmtY/m+91GWms99+lf4rhde5BPPfYKf/fW/xY/8+MfoaXG+oesatFGJFSjXrkNMnjGC+Koo8vWNubXKUMpcAAFl/9KylqozFofivGeeP2PnxMHUWWR4MfoNQ0mlOkMnIKTHYAhEunT/Iia6zVodCBh0ivtOO6qXZ9sog1YZLgYcNT/0/E9xpdhnS/WM9BZVNmO1XBCBo+VtvLbsXXkfNw6/zlP2u3lO55TZiDvLG6CXHGRfJ+BZt9JULaylaRpC6FAmEpwmavEXVERhebyLQRj/X43zIE54xGluOFSCAHX3H9zky1/+I1599Rt0nTQciiKnqiphO/W9JKEav2nuai3+nOv1+oJPXQgBoy15kaFUxFjF7tY286JknGXMi5LHd/cYVRVXdsZUlXgW7l3ZoSgKRmXOdDwhtxllZenytJ5mAuIEpYlJfuQR0DiEQGYNIWiid7JPeIUyCuUdeE8ErMqJxsCwxhqD1WfsMl1IvZHHQGia5AEaKIzm6rhiORmRh8Bia472kbZzPGiX1E2z2ccGJcLgNxZCwGQWZTQmaLp1x9HJMZ/5zGeIfeRjH/thDMIWn823N2nC3wyE+/Myhvky1CiXZX7D0FqjbaSrO8o88o9+4Rf4/S98DhMDs9GEEApOT08pyil37tyh6Rzj8ZjZbEaMikW9EO8Ym7G9vQPASb0iqki9WuOCsNJUhLwsNk3cGAK+68U3M8kGp+VIPk8rTZjlconvPXt7e0Qn9d18+wqTPOP1N9+kzHLWXceHPvQh9h57jPv373NweMR4OmFreoVbd+9hlWY8m1OnM+GqWxOOHHfvVuztXeHhw4ccnxyyOK1pmoZf+5V/zA/9yF/kxZe+m9YJmcEHIzV0mjc+vPfWrHcE4sQIvQuXvvbWlJTzY/ieLAxRHPqnBUYpRqZkngeKIuNwDaGLqLqlzWu8WWO2Z8RcUZ+2LE9qtmcFtszwIeBDxyibMN99DJsZ2rphUS8IYY/l6oSjhwf0fYsLvXR8faDvW0LfMd/aZj6ZoyMsFyes6iWxa3nu8ad4bGeHxWIhMeq9gxya1VoWoLwg00YmY9cxyi1KaVyzQuc5Vucsl0uC61FK0feepmmoErgzGmc8/8IzPPf803z4Iy9gjPgP/M7nfo/79+8n1ktkOp3w/AffpT1aKgAAIABJREFUR98EHj58SNc0PPP0+3nqycf5zhdfYjQZbyZViIq2WwND8eno6hVt32O9xvaBB7fv8clPfZLbt2+jfODxq/tpM8momzX1usF7T921gAajycsKqyO+79AaRnYEzqPLit4H1v2hbCyZo1WRhV+/u67dEbzTEmdHkGQz3zKpCkIBbe0JfStSAgxt6/Ghl0QzFEYHdsZjnn7iKToH1ShjsVxycrJicXJC0zRkWcby6Ih2dcrLBHTfs7V7hWeffT/YjLbvCMBoNE6ZY4agAkYpgofgATN0tD1yFk6de0KiFobN2V4YZTkD2BITDRml0N+EUjB0x40yghKnI6OPSlKkkEQIS5T48+hROIKXZCDp8Ii3kkaB71kfn3D68C7N/Tu4xUNCOEEZQ1aW1GtH21rGo20m+Y5or7NArjXKFngU68WSdd0K4yl0Iq3pM2hiYvms8a4mLxS2GFHkGV5bWdybQE5FTGultWYoJ1AYBqPj9+K4IE86xzQavqfNW42CHzUuU7c5946/VfHx6PX3/GsmoO8R3jFKnb3uhh2VzDgF3BNAJ/iz17BWAOdnraOyoENPbkReous7nH7x09Tas/PBH0AVI2Lf0q2O0dkIlY8IapyAoQCuoYieybPPcnLwEBU87elDRkUGdc1f+9Hv5bjxfPXl17i6PcMYw42bd3h4cErnAm3jNkwalWJ8UeccH87dlrABoGKStqXkFD0YL8ufy8BojI/eLgcAZ9COq+FQBvLMvctjAGVjjBugeJCNqUiSMA+gbkxeGwAh+aiEi4w5FdIhUlKETIpCxgS0hTy3iXEgNbz3Ygiox/d4+RuvE4Ljr/+bf5W79w75no98P3/37/8nTKfP82/9Bz+GNTcI/nXk7JmhosWpQG4Mdd1i1CoV3RHdirS6LKvUafVYk+PxZKXZePRJutGZHMX3zeYQNMzv85+Kcz3euw1Dpu/7zWfadxGVorC1MWQxx2hSwoZ0pn3vyOgJvkO7Fapv8V0jQFkUqr9XEaUkxCEER8ATrWAEeuTJCll/bbru/f19ZrM50QeOjk6oFz2rusNM58yu71NOxoz2tje+JaptMHXNwheM6hWuPcaqNeMiIxhFbzzWRMiKd73ZQopYN0qAfpDaJdJfqA/F6yduDoLCHNHYApZHPX09pqz2+MM/+Brz8Yp893GmL+T8zm9+kU/85A9Rljss/C2KdsRi3fLS+69yevCAXHu2+pbl4Q2M0jyWWWwY81e+50f53a//Hrt7lp/7d/4O/+3P/1dc/cA2452R1GZEjB4khQpneiBioiXqYS5ZVAwMiVoCup+tE+JFm0Ccc2triI6oLAOrTUURLSozgOfDPpe8bZIkS9GjoiESyDBgIjFEQpIty/Nszq3hHmxPDFFAJ6UIRoFf88Mv/A2e336O2cmatjmheXCPe6+9hrl6nePTh5hqznJxQusd5d4HUP0xz072qK3i4eouwTVMMlg1kXy5T+1PmM9zjIk435JVBcFL/b32i7SNabyR2OcYHe/ZzfzSOA+kDJ+jthf38wHMG2LDQwi88vWv8dUv/RGrxQnT0RhtYGdrxqQaYZSmbUVytKzrTToVwHK5YjqdbJh7m3XLeQiR+daUcVlwdWeX/fmU+XjEU3tXefr6NUZlxfWrM8qypBoVm5SpzGhsZqQhpgMZOVppjMpQiBw1JNnmICnEQPRDU8KSpTqBoNCJFUTQGBVplBh2Rx8k6hxJhY0KvE5zL0bsaESlFb7tsGRc292hd1AVJSerhuWiYU3LQpXSJA1+I8eJiR463I8uOPFfHDycQuCVV16hXtT88I/8CD/24/8aWZYlC4qzw/ifliH9Xh/n19W3jpRul2T9Vx/b4ef+9v/AP/vkP6W0MJ1MmI4n3Lx5m2bdkpczjFaUZWQ+nmFsTte2+A465xlVY7xRrOo1neuRddszygtijBR5QZGX9Kon02KY3gXx0SuUoet7otabRqLvHXmes6yX3H9wQG4ziqpk2axpWjg6OhL/pys7vHHrTfb29sirkvHWjPViyde+8TKz2RYuSnpb5hx916JCpGnX3H3wgJc++AHyzNA3NXlW8uDBAz7/uc/SdjUf+fBLdJ2kVJmsQszjg5ypzjF7/zgCy7s13jETxxM3VCMdL178+UPA5a8NjAExNXOMxmPywjIeW6bTKQ8O7mPzglg7VoslJsuYXbtCVhaUo4qiqkQyU3fE3FHXNSrvMMslxibpijVoJ4vsaFIRfYH2kWZd4/xgLCxFWjkqZWNuHLOpmBotl0v29vYIeJpG3NAzrVifo4SdmdhB13RkRX4BvGjrNV3nBCCpJYknRp/MkjTz+TZFkbO/v78pGp9/4f2bvxdFyWQy5uoTj2G15vjghGa95onHn2R/b5edK3P6Tkzm+kQb9KGj6wSIyTPD6ekpIUT2tre5ffcmL7/8dd544w1WiyUA5XiMtZa2reUhK3PyrCBoMabyUeiaFAVlVpBlIss6bhr6dFAZldXQj8U7JxT2dzz9/nRDoj3lQDbQi40S4zVrFGVu6YMX/XemmM1m4r/UNsRkmmWtISst09mM0XhKVS1Znq4SOz7QNA2rdU9RFOw82KIPnvn8CtPtLXk9bUVWkgAVhebMESsIlVkpXLrm81yYwY9mI8dR5xQckTNjLAb5wtnYeJc8ogpSCIDzaDsuzZAEJAWJAiUFB0oWqHZ9yvL0hKZepRSFnrbr6DzEfIxWFTFkuBAplTChTGbl98RA6710mLsOHzpi9Jhgxd8kbR5RI5CMMWiriGhcogwn3EpYIFGkHgIWZ+ke//ndfB813i71953Sgy/TtYfX+pZfC4PkATFiV5qdvMP4nr5d4dslfbaNqY/oD99AqYhffxgXoxxu6yVZgKIoZb4pcO2avl1idOCgDqh8TGEUNA2u92xPc5rFEWU146MvfYhmdYxzjieevMpsvsPRyYrXXr8pCT9BDjUoJXPnEf5Jlzffy/vV27mnm5+LF39O/vvWTf7bMc6DOINJrpgR68QuSowUUiIOb03vGvbtDYNAnUlB0GzMJ1EC4kQ8ISQAxRxQjQru3V3y7HNPsFopXvrwc9w6eYw3bn2D3/3UD/Dv/vR38KU/vEkMyb9H6414RfxqBgmmxJ8OXdQzw+azbqNONPvBg0MOtGcMteGAdL4Te/7zjz6ItGT4o8BFhx4OLmn/V0nuE4ImxEDwnfxcEJ8IbRDAxHuUVvgYMCi0PgecKiUdZAy5NVSleFHYlDQ53R4z354DGjsqWR81FMuaMJox3d4lH1XY0Sz5sHhM1Biv0KaSeHVvwHuMtSL5C56oIzYfgNl3aajUNEF2aaO1SOXOXcOQV6UjRGU264tSCpvD/Ztrpv2TVOoqp0e3mauKD1x/lud/4EP8zu/+Ln/xY9/HZGrwYcUk7uLrA8Yxoz095PThqxJNe3qK6iKrdo2KDpsX7D35FFuzOQenb7B3eo3/9Kf+Oz771d9gnTserl8hr0gRs+LzcZbkpFDabp4PzVlCkcxNJYwilej3m1txNteMkYbE8HUxFU5m/uqMy691lrpBA9gj3D6lfKobDCiJYo50RIyArlGjdPI/ihaFA2VBR9puzV9+/7/PD29/hOXdb3D7la+yOn3A4ZtvYidT7r76JZR36GLE4WrFRz76Fzg6uIvKClqvaVrP1enjtOtDDlaOarzDbHcHFztu91+n8wtad8yy7VE+oKwm1xU+NGL07cX4OSr3LaXI78Vxfp0fPvdhDPv0AOZ771mv16xWK7quw1pLlpuzpKl4llZW13Vq4g5N8Zjk6A7vpdEnKbTymqOiZDqdsD3fYn//CjuzKY/vP8b+7i5lWTIZm43lgtay5hkr0hVjBMiJA7AfIOpIcJ5oFEHpVDjGszmajPFtWgdlvibvP3y6FyLJleJNpLXDvDVap6RVjXLuAvN3VJSURUaVZxRZTpFbWTOD3LPghfv5KIJADBHvA7GPFNZgrCV6ODk54caNG6xWKyaTCSrLH/lZ/Xke3xxYEDlm37d4etY1/MEXf5fMeKpqzGP7e5yenLCua7Q2jMsRx80xJrN4JZJgMalOZ5CBvesivY/oqLEarBZJYG6FGSvM5MHHFHzX45XeMKyUUkQj56cQAjqzKBspcjHd7kNPlhUs1ita1zPemtG2Lbfu3mFUiHmxC57RaMS6riXRUmcoa7BW4/uAsRl9322eg2XbsjWb8uDBA7SCW7duceON13ji2efpevGpjaFHxezsnsa3F+Txbo13DOKYdNI0hLfUAjHGDQINZzSuTdd3kEVEx/72nNXRIatQkmlF0y65Or9GsB3HxyucicxXVxlXc2Zbu4QG1sdL1m2DC4GDgyNOlj3hzn28dxRVznQ+SRtnYDqeCUCBplmuWJ56QuixmciWSlPgklfKgKCfHB1z7fHHuTa9zsliybpZiVRED8kY4J0X1kJVcufeXUZxtNG2KmWoa6FmZflUzFtVJMvk9zvnWCxOOK8JX6/XXHvscfb3hI4+n88xmaVzjqoo2NnZYb1cUdgCF3pu37tPZjKiihKr7nuqqqLrOpq+oTAS26Zzza1bt/jlX/xFvvb1r7NcLbBaNpAsV4zHYyl2tWE+m7G1tcUQ3dp0LU3T0TQNzbqmbXuKopIJrBX1skEZzfZsxmw05eHBAa717+rRWh4foRprZdG2pMxy8qzE5hlELxI31+OiFGCaJJewedq5IseLBbNqTJxG8jynKApO1QqjDeuVdHCLouT+vYcok3Hl+Ii6cbzwoQ+wtbXDeDJKc8RS5Dp1MdjMh4EOnaUF7Qzx08IuOQfEDOwB+cu39kNX51kGiTVw7qiNQd6iUmICmsRSgFBlo4I+DIdxkzrEgdXpKTdv3uDg3n2WywUni2Pa0yO6kKFNz861bTyWfGQlEtcaMqXpnXTx16ua9bqh68TMTCEFgcZAFpKpag6EtLFbFDkxsvE1ksNWSrlQMR0/NSEiz857TEuvEPAA0scXzzS0A21eKUVQYaPtBolRRp19P8aLxcVwSBi8Z8Q/Rg7k4t+BFE3xnGSLt4IH6sJrpCcnJYSBrM8ZGSEYdJEDkaZbU2WWPLNsTzIqEyF4sqwX1krfE9cnROdYLk8wqxq3XtB1p2QTzVhZvvraPXRV8dh31tT3H6AIjMuCVXNE7xpUVogkxlpWizWj0Yiivi/X6g2T2YTOOeoGQjggCzU232Y0inR1ZDoveWw+Rj+zx0eeu8Zv/ObnOHh4yvZ8RzrdviWkzdckuVQkYLzZRKQPEaZxI5tIyRiDt0U8u59Gn93P4T4PgKNKhWvwjg35SSHyo3cZc5Tn/OwhUTqCDfJH+83cjN7jYyTgxQwXWR0CUeaqCvL8aRAzV8SfRKU1LTNokwxatayVRmkxxyTi+ieZTwyT9+/zyo079D5w5doT/K3//G/zK7/yK/zfv/Tr/MzfnPI3fvqn+a4PX+G3P/nLWEp2ps+gSsX6+JhceWz0rBYdW0UUY2utqbseazOKzKa0y7DpuMLZnPfe46JIx1wQKn5mIsEkX5EIKjoUHYWRMGtlFF6r5FvjwBuU1piYQdcRVMQaLfdDRTIdyF2HY0mVNxTaU/iO1kc6r3BeYlX72OPjCkykmIyIqhAA22pmO9uApsAyG08Ybe1SjiZobZhhWdU1W21H1AUhK8mKinyab/ZkVkvCasVouWLL77NaeMJqCXjIMqITqaH1Atm9e3NR7pHSCqskwEEbkaIIaDF4onnhWsaAtgbfK26+fI/Vbc/T738f5ZWaqF+mO1jxVz7+cYI33P7Kgn/74/8hi8WC0C3oQ0tZZITlivbkkP6NNzk6uM2N01M++KEf5He/9BnoI8G+ynw6Y/LVz7O1d41rTzzDG7/9j3n6+/8SP/XST/Kr936Vtt5CVzmtPiHSEzXkwRC1xLmjgJjJ96KwZ8TaJ2K0GBoDRFzaexMADqDFR8SaxO6IiQFtZU6Kf8zwo5Zgetk/oyZEl/aXs8Ny0IroNRFLoJPQcyWSQ61yrII2ttThlP/oxZ/lenWVr/zSz/EPP/8POKlb+mZFNdvi2Wef5eWvfYW2bglZxXzSAoHf+e3fYLS1TzbaYtF1tJ1HZZbrz75Ac3tNkzcc9vcYVxm3DldUzxwSnZFroyW2UYAmlaOCl7U5DADse+Mg9HbGYFy9AZEjDKKqoM7kPpmxcqBddtx68w1ee/0VAIp8hMkznDesWolrNzajTwBF13VpzQpkmaWumwTeyGToe4fRjsf2d3n/E9fYns740FNP8IGnn2JnNmU2rtjdmYkHaCXnmjw/J203UnM4lXyqckng8amZoq3ChR6FQusMPTCPUrz3ICcZZKjBKkjeJyF4Sq0IqY6JXtEHl56DgAoBHT0a6FWGMYrJVqBra1zsmIxzep9zfXfOweERquuwvUN7R65lDY0q4n2PDpEYQPtI1Vt8jHR0tL7b4EchBl557WXu3XoTc/06s/3rqR6PiXU7eFKdZ0z/y98cTP7nFwCcwFkK4DCUkjPAbFryD//B/8r2zpStac7e1piiqLh/7x77u1extkSbEav1AXkZ6ToB4LyPdE5IB0op1otaPGFFFyES5BAp8lz8l7ynCzW+7yEEciufm0YYrrEXIBql6dJ8K0YVJ8fH9C4wmU05XC6pC8d0f4+2bbl/eETrA5PJhKZd0HWOQVmwWtUsm2P2t7awecZ4MufurZtYDWac07RLrNXcu3ePj3x4l/3dHQ5PDuh9x9/7e/8L/83P/CxVVdE6YR0LGG8x8kBy1npI93hjQP/uM7z+hJ44lyji5w4N3rnNQfS854JKHRkVFPP5lL3tLdr1Ka8f3cATmYx3qKqKarZNmPdEYzhcnaBmYzKdoWzGZGvOWM15cHxI23libNFZFDBmKWwJm2nqesWDu3eJMbI9Gm0Wx2ky4JWkGM9oPkZnlnI8YlxWLBYLbt26xfb2NtpmGGUl7agc4Zxjebogz3OUgqZpGY/HopuLwoyRA4kW1M8FxuOKtm2ZzWYopTg9PWWxWNH3Yui5u7vLdCrmwV3f4hvPer3EWom/NspLckFwLOuWpreU5Uji+rqO08UxeZ7TLTs0gUybRJ3UHB4e8hu/9uvcv3+fyWQi9NtMit4sMxsXfXkPsvlM5zO01hR+xGwWN0ZrwXlWiyXb23N8VCzrm6yOV0xnW9RNx4MHR+lw+e7COFZHQvTEFNWYj8bs7+/j6iVtjARribrAh57T1ZqT41NAUWY5Vov5cVkU6CJjvVpRNx113YrRnMqwVorkoigZVxPW64b21n1OTtes6pbr16/z3AvPC9KvzgwbSTRhbTTBSzduiFZVDPr5gavKhaJaXX62Lgx96YB+7vna3JXUJYp9ovMnVFtZ6X5q6Sr6BNhFJYZmmQHnPa/feIXXX32Nvq7xTrxuympKpjJMOabrPbPtGZPJBK00bd+xaB2L09MNi0th0Cons5oYRK+vrSK0nWyaqUuJzdEqQ1GA6+mjTwt4SN4TFzuZ6c3xXttsH1WGbrq0F5ggb/+6LwM53+xr5//+KGbNZVbFkKR2+feuuxajLP2qJrOKvZ0RY7dAhZ78eI0JLSp6Qnsi3TmjGOcJ2Gk9v/Ub/4L69IjvfO461fURD+8+pDt4yFIFbHPAvBdgr3cz9h57mlUXqI/uY41BZSWl1tAJmyGGQIjiuxO6ntK0tK4ANaGLsOYBFduUuRHAsFmSx8gP/+CHuHf/kD/80uvC2lIlZSa6/M65tMEKe09S087SvwZfFCnsvPjaMDDChj0jgTlmkCtw3v3u7J76S5/Du31OkQsjBIU2IvkQM0qRaQRx8BJJD1GkIek5i0aAwkBMUa8RlE8JVgl4TB5WUXuCkmLdqCx54TSbQ8ConONVTlEaPvCBD+Bd4Gh9yqpv+fhPfJzX3rzB66/d4u/+nV/kv/4v/ws+9rH/mK999fNsXbHYbExobtIFhXEKr1KH21jqtmO5XLN95QraWgqT4XuHVkr2S1LtoRQKTZblosXve3lvwW9Cn62RA5nRGUF1G28kEz1aRww+AYAB5T3Rg7E2Mb0EfCAGcEuyWKNiDQSKTNb36ISZGIPMqywzaB0J0aLLEVlekG3tMbkingK5yhgVJfPdLQJWmgNlxZRI1zq6oKh7T0Rjx9MNQ6iyI8hr5m1L1x+C6ujDKVZ1gE4SLkXwvQDr7+IYZA6bAlepi8lIKplh64yHt45QZOw/+RhjDc//2Jh6taZrFXv5kzz3wetsFy9y54077F57k/sHX6AMBVM1QcURxrS4LKMajXj94A5f+vIrLLo1v/6Zl/nJn/gEL370u/j8F36P17/xNdb1Cc8cnfL7n/0s1/en/y93bxJjWZKd6X023elNPnt4TJkRlWNlsVjFZpWKZHOQWqLYlLqlXrUAgdqoAa4ECBC0lwAB2mglbbRQL3qlhSCoKXZTIqmBzWKTTFZVVg41ZEZmxhw+Pnd/8x3NTAu777lHZGRNbGZVywBHZrz53mvX7Jz//Of/OTt6zPZGj1//2t/nj9QQP44pTM08K2lUuSqciJYt5D04YnRbfHGu3b/9ZV2fJVgosaoFLpZtn8LhpEc27VoiPFK2DlneI4RB4Ihl0K7B2eCU5yXWNSih8QQxZqeD8HFr84AW4G2GQJKT849+/r9h23X54M//gP/7w3f5/uNDJqczFosFr758m+ODfb7x5l/x5p2H/MobLyC8Qy7gfDJFK4M4HuK9Z+f6C8ikx/H+Ph9/cAeSDns3b9GPtiipuH19i0cjSX89IucDGuda4EnhKUIqJALwYfE/JN752RtCiJYRI3G25jJYXtd10ERrPEoKJuMz3n33HQ739zFKgwyxZxRFgYngHM6FFqpQkG2B9CUYqC6K4BBay+M0YX1zh729a6x1O+xsbtLNUuJI0U0MnViHokJr+4wUSFRgPquQOIdjME+d+WWb9TJGeTZ3W75mudtZa/E+xBIrdqFfFjFtWz2UzxQXw/EYFdJO78L8dmmHwcCj45RZI9k+GWO05kmZsyhzWLb34ltNlQsW1IXr5+qnI1rm0fn5Oefn5/T7fbKNC9Hp1bGIpztHfraiyh9tfIIN4glOp/KyqUqrFSgudZI4x9bmGg8efcC33vwGippeJ+V8OOZ4eI8kSvDWMZ1OqJocEwVwxDcebRRee4qiABmEIUpb0/jgbuxryISmcME4RWtNWQdWlfWOuqyQQhJFrYRDbYNAfxKDF1hR4BqLkpKs03k6rhWhdTXSiiiJGA6HTM5HvPzaq5ycnDCaTthkDaEFaRJxNj3HCMnW+ho7W1tMJiO8F5yPJuzu7HDl+i4PHz0iyzLKwoJYMBuN+L/++P/gV375V8k6a8GB0gucUEETDXi2kryKpWGFf/w4Yd9fx8nqryVsvPoBl26O0AZyQVW+bFkrReiJjJBkUYxWsChKdBJz7doeiUiIdERT5cyLnHm+YNNLlAl9nUKHI62qinzeYCJLZyAxJmi4GKXxOBaLOaPzMVkUUQhN07jguNQyVpbq/FoqoiQmTVOMNiv6o1IKE1/Y+y1FkRfz+WrBqqqKJErCwtICOHWZr6qBdVmSxjE+CkBAEDCrQjDVLkpNVSC1XiV7ZV1h64YkSdBatpRb+xS7yblgp13X9Yqy2TQVnTQlTVOkFJycnHDw+AkHBwcgodPv4HywYe11Mvq9TvguW5F1OytAJzYR1gdtneV1S5IED1TFMumBWBuSKMZEiul4xunwrGVAfXZjiTZb59qecVAyMGmUCQLPsdBY6VBWYKqSJJJYL4ImgAgK+7K11FZKkaadIGQpTauBVKzOs1eSJq+ofYn3nuHRMdLD9nawbkdetgNtf6PwrfnDBQ3bu+cgtZcSQZRv9V+eF2hfbLJLyu1yLMGb1fmRHlzz1LlaLTSCpSJPyzywOOGpygWnJ4eMxqethk5wOjBpROF0C1iGRKmuLYWtaCqLbyx11WCXjBAV3EgcIlQrXRVoQYBzS9vq8DnKhPvDclHNWm1A//oU6Z4KCpbjJ6VcPu89n/Y5P8prnwWBnn0sVINBS89aLEk0pHaKXARtGulyfJ3jbIMyEdZZqrLC5hbXFIzuvM9H3/kOShpeeWGX6sE9VAXD4SlXXv4co/Mhbr7ARClCNoxOhwgVk2qYLmbsDNa5/+gQZVKSzSawWVyYf8pWFOU5XmRAipAxvsopxQAvPFHWQbU6JmupR8t1PvrwHnmxAJnibRu0ChGcEgTPBbHwF85yT99Zl8CZpVBT2Knbx57eQgNg+kknxs92LL8zVIuWls7ehwbYADa3FvICLBaEC8uQD2tq07J0AiD89Pm63Fq1ZJC5dg1eMgi9F0jVASEDsCsFURRxbf0ajV1QFTm//uu/ytnwn3I1gq//2R9x8x/+DnvXX0GxwEvB2sYup0cHQMXa1g5xkrJYLNBRvGqvhpAcCSVxrgVNL4mKQ4hHfFt59D5oAC1t0/HLFjEVhO3rcJ21bHWOEC2wE86rcw7paAGjcI6Fs0iWQu0utMmKAM7LVaLa6sKocO4aL0AH+/S0N6DTW8dLFQoxJkYlGVookAoZZWijkUmDrC2+bgE33bYISBGKTsYiY4M0URANbylhihY0AfxnbAkkRNBHEAAirPFh3kicCIxZoSCSMbNpga0bXnn585zuL+hdKzg9zdntXWVTXiFKtkhjwYP97/Dxx+/z6leuclLs0406dH1FUWhioCkamnnO7pWbHB7PYDLhdDLiL978E2ZnZ/zKb/0W13Y2eOftb3I2WzCeV8ijI4q6BSy/8c/ZvH2LWnvGuUJ4jxmAEwrna5SQ2HZtly2j1jmLVhrvJVJc3H+OpbirR/oArHoRANRQ2PR4RWgTFjUQt0BXYMVBYB0sK8BeXLj1OAdChnt1xQYM3M+gbyIarK350pW/S+9kzkd3/5LH9z4ElXDy5DFneYPCcXZ2xkePH3EyKfj8izcoSkdT5rz/8ROMCkUCnwPBAAAgAElEQVS/bkcQSclkNOT4/C6DzQEex4cf7nP/8Tl/57f+DqdPHiBeiFF+TlEGbUuWLdue1pTKYn0FQhF4Gf96pM7P7hdhHXn6NUKIVmLCI5xjNDoLmpZVtXLzlGqpnXMhz1DX9UpMffk5l9da78M9o7Wm0xu0LVISLSRKSowCLQWxEURGI4Rv2Tbt3iYv1mwplszJT573ZZvn6v+f9/xzHr8M8ixjO9/uJWEvkjhhn3ZKvXR8gTEU0XhIkoSsk6ykIZQIsUnQmhNo7YOYLuIillyep5Zp71wA1IbDIePxeNWmtozRLrc1PgvkfNZDPTP/Py1mdOIHXJNn3i9kEJu2XiBQOKlwBOfGupojGkekJfVszHt/+uf00FgE5axhbiucE6g44f6db2OiiG6/QywScBLvoKhyKtugsoTCNaQmIe53qccLfFOSmATjBYu8ojKW0+kk7JuRoSlLTJriG4tw0I9TRF0xqQtMJKmcJTURcT/l9Pws7IVKYJRGOom0lqyb4BpJXVuu9AdMJhNGB4esDQZIKZnXJVGkgYZIC4rZjCJS9NIE0elSFzlOGmZ5QVlLnDQcDU+5urPJ/sFjNpMb3Pv2t3jjhZusv5RiTUJlLUJr6sa263sA55fjWX0sIBhtfEos/ux8+9HUMp8/fgIQ55PV/8vDLxMxLhKbi8qlwNuKzd6Aa7u7RKqhNprrN6/z4suv8+ThEfPJlJPhIZP5DDPoI7UJ1t5xh+HJAVIrFkXFaDpH6Cm1K+j1BvR6PcqmQDSOtf4GsTQILynzPDhSKY3ShlgqTJaQpIGWbetgmyyjmLIqOT05oSgKoigJx4JYKWtPJ3OiKFod3/b2dlhMVaAJh/dFKCE4G88wxpCmCVUx5/x8TFmWLfroSdKE4fAEEKyvr2MEnLWTPUsilAgW13mek0QxVRVU2ueLCfmiQGlJmsYr94Msi6mqkve//30OnxwwGp0zPD6hKUriOKbb7WK9Y21jncFaqG7ZumF7extk697V1JR1xWwawCop5UpULeumLBYziqLC1w2b21sM+pvcv/uAx48f45/d0f7Gx9LlQSEUGExAiIVGiogkhcSAcxWVlSgDEy0DG6AMEEYnzahwKGdJ4gFpp4tAkZcl49EcoAVzolZgLmy0urKcHB9ydHTUBqWCwWDAYNADPFWr4aCUQusI70P1U0qNFxeaLn61qbV/gJJtSeGHnM4LVsEzAqzth0plQ1Dog7Wwc00rJhn0CISEyjco65HKsZhPODs+4vD4EdPxMCDOtgIapO4Qy4i8snQG3WDNtyjRJrgsxCZhY11R1BWj8ynzMsc2HlyN9G7VcmJd3VabBFolREkXI+PATvINsdEkSYIwrd5AyxcAgu3lzyqocyk4+bSWJvjxwtVnGTSf9tizj19+DC70QJ6yV7702Op3E5PRsNlMEfMzZnc+JMu6gKOxjsV8Sl3XdNe3MSbGlyXT82MWkwkffOseQgy4en0XVZ+hsHTjNb753gH//he/wmgyZ2vrCvO8wAiBKUY0xYLD8Yi96zc4OnxCNlij19+gnE/xdYXAoXF410BjKZsn+GpClOyiKEkjx+LcorIOaEPWHVAUFcaVfPGVTUanIybjOZMqxjYO6wksTOuQPrhS4T22aYKF9rLS7QO7SLbV8qDH0ibNdilKeiGyuHTrgsuBMqtg+KcRGgb2oEFpiRLyKWvjZdDrvW+ZOKG65VqQ1XpBjaNu26W9kiBVazsbwAKpDVIGmr+QHu+D25yQAqGgaRzCOhaTmt2eoZMakriLbWA6GjIpFqjIsHP1Or/7u7/Lgzt3eO/jB/y3/91/xVe/+kv8rS99DWzJzu4tzk4neBUxm5f01wYIpZktCqQ2lHWwQPXOoaMY14TE2NpqdeZrGxp3vBc0bcujFjqsi9KHhMYDtELOwhHFEiMEEkfTWJQKMY1zDusD80ZYAgDkG/AWqZd6f4HKr6REuDzYi9eBydUAXmmkkmglcUmKSbqknQFZfw2vJLEJjptZNwMZIYRCmwiHR3uPKEtk1N7HKglz1Dlio7FOQ6RBJXgRYb3B+TlCiuDeiMPVnzWTMQS8q/ZSEUSvkQIjQvLgG0sxj4gSw+de3uatN9/CA6986QoDaZh8NKd++R4vb/eQvkd9esiv/pu/gatnjPJ9RuMjOlFDMxyzVjV88M63GQ5PSGSfzAgezaZsbnf57sdHvH/4Jh/cf5teZ8CNK1d568F36XRSHk8bHpzd53zUY+P8lK9s7tHb2OEsm+NmGTbLmEYTjI7B5UhByzptdUFkcOGSS0aOCOw2KUTL+gqFO7kK3pdrRqsjQqgwByZOW03HBiCP9n71dWB52xrnm5YJJi7csZRBOIuQXbwArdf5jRv/EesPJ/zhH/9PdAdrPN4/4N3vvs+VF25xLZHMzqd85+5d5pVnu9fhyWhIsYA4kvQ7gXW+3s8wUZfpfMa9J4/43O0XsQ5e/MJ1Xvvq65xPTvln//s/xYqE7aMdfvnX/zZRV/L20QkiHYHURFLRuJxwxmSYFc/RK/tZHcsc5nkFkRXDRCqaqiZWoRvgg++9x3g0oipL4jgiigxZtxvcU0VwuKvLmvl0RlEUKHVR7HtWkkIIERyt0j5RHBxSlQSNJRKOXixR0mObAqUEOuqxbKFSrf2nA3QUfwIouMwiELLVRbsUbElxyaiBiwT02Rbtuq4u9kIHtLGuXwE5LePVhTUztASGlvwoifFSMejk7Kz16cSavemA8/NTGtugEHgVwAjXlCsmaVizW9CzvbW0DizJ4+NjRqPRSgRfiMBuejpGC38/JQznE2MZczyb5PvngG6hpflp63TpYSUYLyS27ZBQSlEWJZ1uB19X9Dsd/vH/+D/wnb/6K65fvcbdR/eA0K6ZJAknJ0d0B2tsbm7iXEOWdUlUh5OTE+ZFSZrGnJ5NyasiAG+RoY5NyHukII0Tiia0Odu8pMJhnKGoSva6fRpnUULSy7qUfkosPVGSsJiMGc/mdL3n2rVrlHnBaDQiyyKiNGYxmxArw63bL5GXDcPhCXEcMxqPca0zpcEwm0xAeLI4XeWxjUmIogijJN0kpTcYsL+/z97uFbxr6KQxN65eYzqd0k2nvPv2W3zulc+TlzlORFSuQmsJfik0vgRznj8ug4XPPg48HZN/lkyci5/0fNlUEbK1IHTr25vXLfs6BXHU4ec+/wavvPQSkyd32L2yyfXrV6lKx5ODQ0Znp5T5HJMYtq9eYTJbMBsvqOZzDoaHKB2RpB26wiBk8IQfDo+DeJdWzIucvb1ddnf3qGvLYjYn7XTCjWxrnIDFYo5JE6Rz9Ac9jEmoqoqTkxOQgu54Qm99IzBg8IhJqFo6F8Q8E5USxTGLxSJYg6cxxmi8D4Ja3jq0kYzHS+DGrJgtZRko3Wma0l0u6N7hlUTr0L4FnrqpaMqK+XyGSxuqsqZuCmydg0zawDSi280YdHsc7h9xsL/Pe2+/w8nRMYvFgmIyI+1kpGkaFLy1wivP2emITpawc+NasD30EqM0eVUh2kWkLMsgjGYisI7YGGwdxLDOJmOudPu4xnM8PGE6nRJHHcpm/pPPxB9zCOGxrsZ5h8agTIxWBqRCRYY0iYhUg0WR2IbJvEYbgZKGLFYoBHXdIHw41qPjA4yOEUpTV47GCoqioCyDy9LWxjZpnFGWeRAHXJTM8gXvvPMOdVFy89ZN3njjDaJYY5ShZklfbFYJs5ItzZQlDfdiwxSypVoTWpz8c/pYEeqpzaaFa9pe4XaTdTYkXKJGCR+q7OhQaXfQNu4jnMd4G5I3YTnef8jjRw9YzObgLXm+wNcVShmaFgjSJsICnSQj6azRTQKIKIRCOXBSEaeWyoIUFotG1y5UFF1FXRbUTY2UAaxJTRfvIpz3FEVDp9enPxiQtuwc5yxCtRWWVeXrZ2S3vTQ8nqbVPmpjAqQXl7S0lo4IraKzgBUY7g0ggvhoO56uFAWb2WeBGHiGeSXaCuySWbNk7gnwjQ3sBC+onaeDZV41oGLqpoQ6ZzOCfvmYe+/9y+AGEG9RnTymaRoGvQ5azMhihZ0OmeYCmpLJaMp0lHP3yJIMMgp/ytUXf47Zo3vMZ+d87lbGVuqwkwn+yg5JpHCzgpyKRjR01zKwFbKw9LIei7MDrB2zmWwgrcQqmMmSTryJ9BXSzbCTBVqsMy2OULqmngh0sgbuOk1vgFrb5EXXYTL9Hlz5mA/mX+J01pDNjzirFXPRJammq8SnsRbXCubSWlErHdpLXctcQVyI4gourJED1lqtKoLLgkXT2rrTvvezRh+lkBhjUDKwReLWkQRsCHxpBYC9ats+ggZOYOooNAIl49Xc1SbQs6UEpYMQ+YqJQ5jP3jfgNd6mWFfivEV5cH6fprwJpiGOJMNI8urnPk9Vlhw+2Wc+n3Pj5c8zLS1v/sVf8j//k3eJ44jN3hqD117F6pire3s8eHCPa2KXYOFcMVtMuLp3jaIBaxv6kaBpKrSOViKJECqyEsm8zEFFOF8htcHVc5K0Qy0MGs3jRwdsrWdEOgKr0S707i/1f5yzOFuGynAdrMwap6iFpKqC8KxRCmehsR5fWhaNo7KORR7Yt0IraCxaB7txIw3eOoxOiLN1pFZEqUEqkDJdrRvGaLwObFwrJLYOelYeh5BhH2u8w1mPagReGKROsU2C9z2EznEuGDtI/9mvoJFZNvBdYl5IgxKKxx+O2N7ZQ5gRUZzy9pt3uP3GNWxpOfgebO95bn3pRW50X2XRHCKbBrVmeTL5Hjuyz63sFWyvxj1+xEDG3Lt/h3pWIir43771DnGSsdOtOTzNEVKRGs2D45JBNkbTcPPGVd776CGDWOOF4vD0jDjWfP+tN7l963W+9nNfYLIpeK++S9YkOC2IO4bGV9S+ItMR02rGUmNCBcJMKO44wDukXLaRAVgq7xEtICw8NDJcG0SYI8IF0Esh8L5s54FCCYVrlg54BtkCqPiWIeQiYpPSMYpXB3+PL6g3ePj2n/Hnb3+bl17/Od75zrdYVDmdjT5+PuR//fpd4lQhvEFR0VQ1m4MuO1eTACY2ntI23HlyTlE9Ym99ne11Qz45I1nrMa0fYqM1uq+k/OZrr3D04YwP3zznva9/nc7aDq/+/Jc5je6C7AbNRvEEIRSVCIK9zlXwU86eL7MzVkDFMyAFXLQXXQZYlF7uxcH5CzxaKeoqZzY554P3321zk8CYX9/YCjlFY5kvZihpKMucxWL2VPvS5d+0FDW2NuhnSjPDbayjfChyRMphhMXZisikKwa/FWrlJhi6ez3KREgRUr7aNZda/njq2C4f97Nxh/OX23LcshzZno+IpnX2A49G4YVtwcZljCJRUuCawMoWS2YQNQJIjWStE6NFw96gx32jgsupD234VVOF4sula/WJMFkExvtsNuP+/fvs7Oywd/sWmUqCtEHrQilb9ia+BXF+ClPRPic2EM+wOqSH5pLY+yruEzzNBvOSRthWPD4s9FLK1WuiSCPKBinhwf27vPfed7myu8vd+/fIqxytNVk35fj4mLPzM77yb/wiadJh/+Axg/46w+Mz1tc3mR/s05RB8L/f75PEMWfn5wgaulkHX+XIlgHmJVRNhY4Mg06HuiywWMoiRyLItrdDx8zRISaJMIXmxvoeo8mETBvWNzKa2RxlHTbP6SYZh4eHHO7v82u/9mvURW9VyJjkc4QQNLVjtpghkcQ6wgnHfD5HI9hcH5AlPeq65nj/iP39x7x44zq3X3iR8ficF154gcePH9I4y3vvvMu/9x9WeKGQSrbzRLbMuB8+YT6N3bV0R1v+dxlb/qQ8iL9GO9WnI1DOObxbCmldLIJCK9IoIut1cS607WRpROM8p6cnnA7PsWWFMQZtAqBRF3OEk8h6iU46qqamKAq0EQjRVrmEx3hBvih5/Hif6EYc1LS9W6m0N1WwhK6bhrIsqW3D1tYW1tbMZrNW/yahcfXKCaMqyyAWJQSRSYJwo2SFgAqCyraKIkC0IEygPi7bpur6oloSQBtLVVWBHYPHN8G9IklimiYg/nmeo1gK4lqk8iivsDYiTYPVuTGGOI7xLliRn5ycrJDLfD5H2PA7TBwFrZ7I4Hyzss9OsmxlJWviQM0uimB1mOc5Sil6a128D4KVq+/zHq0jqqpmOpnRNI4oMZ9prhIolAENda06vtYRvU6XcdpB4dHkKNq+cy1IYoX3GuEUrg5td3kd6PY0Ht+TRFLQ7fYwaRdb1UynU+r66STNtm5lsTbMxxMODw5QkWJ37wrrgzW6XYN4BnABVhvgckgR6NZetrUKKXDIi7v5WWqld08ZbK/aA3wQSvRtW4MAnBdtW4NCENo+vPfgAj3XitD2ofDUiwXn5+eMz87Ji+Bg5tpqibWa2taIhgDKpANkFNPpJuBgeHSC9wKtIhrnKEsLUgOeyEm8rvBWPGVrrbRCSYNWEd5rGldgG4fWOrTDqUt04mUlx4kLwdifsSFYmjk8Tcv9Qa1Uzy7wP6j16tMev0wF/kFsnIt/Bye3WQ3CRNRljqZmb3uALgsevn2X6ck5nTgh6ys2EkG/2+f8/BRjLFLELBYTylxSV4sA4kwKZGRQCl68eZXh0T6ialjkBa+88grGhN7704MjRuczrl+9yfh8hJSCjevXOT2fYLygmAzRSlFUC87riLL0JNs7qGzArKhBJWgivF9gXbCd9o0lEL4KVL7ARAlSGEgjRL9P8bikN4BZDflCEWmNsRftsMv7eVmpWa7Xz1bClmBGXYdk6/Lzn2YbvwruBXz2FLIW3pWhDSOskxf79bNtZN4tmTh+FWyH918AkLJtWXoaYGy/y18A0iCCsKRzQYy8TQqMCXNkM0r46N33QhtqmnJ+cEjazZBYcBZX1xw+eYjfahDiFTbXN3DOsbm5ifeCs9GItbUN4iRt1+KLpOKym9Xy2mqtmYzOSdOUxWy2ak3udnrkVU2W9Xjv22+xmE/Z3riF0IrpdM61nQ1cXVFM8lVSAuHaO+vattKg3WWdp6ibYPXcgG3A2cBga1xwBKudx5U1XliMswiTkgr/1DkOrjHqKfD32XUiPLfUtvrklddat+8NIp5N0wpU+5Aw2CXl8zMeS5B5yaA1SvKtr3/Ezt4A68fEUYqvUq7fXuPsUYGrBC9+IYhSGhPhyNnpvspWtkUxPmDupkzrM6JmTlXVKAx1U7C7c4PR0Yh5A1/9wku89fEx00Jw+5rkW/cWnM5y1rsJvqip6oJ6oYjimCiJEd6icZyNpygtMA/usHn1Grt7t7lbdIi3OhSVpZJjpHAYr4IrmQzwqBYaJ1txZi/xolm1VgXWnsVZMEDTgqd+Rct3wTrcWiS+bXtZhuee4MJ5CWwgMOh8mymF+W54ZfBlMtHli8nP8+E3/5BmPOXk5ARjFN9555sgYhoU58WUTi8jL2siUXN1a8BaN2F9fZ3KCebjEdOm5PA85+R0zu0bfURT0DSCPJ9TiZJrL7xGnKVU1tFJe3zhtW2+vLPG7/+Lb/KtN9/ka1df4LUvvMJoesi0nlA2JUK3uYCUqM+cvf3J8WxrzV9nBBv4YCpRlwXz6RjpWbULqfaeXjI7hfTYql7tK8//fcvf2AI73qNEcGJKIkOWxIGRo8J6p4wO6+Izx+hblzXnQttsKCo+7xgu0cO5tEc8w/R+/vH7ZQ2LlRyAl61bWmit8rQ5kG21+YTEs8wT271CBZA7jQyp0UEAvF6ueRdtLKJlaz87msZB21I1m82YTqermH15vVfstX9F1/5f5Xg2brOf8tyz8R4EB0IvfBuDs8pdnWvQSmFtiRGS+/fvI7yjqCuqOpgSZN0Op6cnHB0dsb6xhlahmJpkWZu3erIsQ2tNnudIKemmGVIK6qoC55Da0rS/SwqPJcw5rTViOeGa4AIplCRJEpJexOl0HIgNTc3m5mb4vrqh0+2zub4BjeV8fAbA7pVtpmcT7n70IZ/73KvkRcH5ZBL2PRk6C4yJ8Na1AhQKSci7JcGluLF1AHakoa5r+mtdRuen6FhjIk0nzTg6OWY6nhANkpWmUJh/f30G4WWwWEoZiC8/4fgxQZwfvvtLKamtxVlLZCSqVS/33qGFYjDocfuVV4mUx8caVMT52YiD4YLZbIavG6woEAoOJzPqWtIxGTvbG1y5sotJYhofkecVVbmgtoEtYSvL5pVtdKypyobSNjRlENOUWRYQLwFKGTr9AZPZmKIqmc5nSKERQtFNuxyfHmPShGs3buF9cICKogipQtUy2E5XpKmlvzagLGpOToekacqgN2DQGYAUVFVBr9cLgsizGUIosiSi0+kCktPTE4SHRBuG50O63S5aKpQJFPiyLOmmGWkSMR1PQqXNBYZPJ43DzRRH2Krm+3c+5NHDh5zsH/Lk4WMMimywESqS3lEVOU+ePGEwGBCnQeRxMpnw5MmTIATcNIynU1wTrNutrYljQyfNVsGxw7K2tkbW6XA4PCdNY/KiYT7PQ6BYXQhafybD+zbZF1Q2AG4mTdi6epXp+ZCmXISkGodzkm7WwTWWoqhY5HN844LwLgKkprs5wCQdvIXa1hTFAulD0I6vUcrQ7wdLu8Oj/eCIhSdSkuODfc5GpwC8cOtFXn/1teCQRdh88UtL38DGEEJgqQOpuK2Ag6NxHiG7q4RrCfpIWCVDjuXnLasQLli2LtkaArx3GBRNq8iAa4MA3wopC9o2qgA6Hj15zOO799l/8ojhwTGxUlQF2MJjYkh6HcoGrA0bpLWO0fk0aDeZmPk8ZzIJ1ciqcZR1oHOmSgYSrxO4JuhuKGlIYkOWZXS7A8pZgbMS613bfpgitArCttRtSLTEtdxTyejPyvBcXJ+LIEisWjCWiPvzgBvE0z3asNycn96kn22let6ddvn5y5W9ULO3WB8qdgsMkSt4oe/p4jh78BZ2PmT66AmzU4/tlFy/KSnORpyfHNHNUmxhefjkGOUl86ljNB8zL2rKwnP/ZMh/+nd/m1SdoKsZT06mvPX2O/xbv/XbPHjwgL1bt9gSHdJBxGQ+YX42oq8jrDwmkxK0Dm2OzqLXbmJ2tojiGKxnLe7h9wTNQuLrhtnhHZriEUm2Sz4NwYB1OZUc0ilyIpkyeeUN4o3XWLz7TW7ujul1d/iz8xmJrVmvZ4xpxVYJ+mpL5uHyXF92IVn++/L5vUx1X4k6wlPPrYJSfmDc+zc2tFJEui04tNayl4+hLa2EfXF1rIJWsxepQpFASgleI7VGqjZxXIkbtO14rkEL0352jWgk0mlkEiF1gvWBERgnBnFWEyN45xvfZOfKLntXrzA6HfLSzT3+0e/8Q/YPjrj7+BGjoyNevnWNrStbnJ6esr2zx2w2Q4qIbm8DZGiNDULBmqKY4BuHtTXGmFUxRanQCz+ZTej1BgghmMwWFC5CxRm//8d/wo29HV56/Raz8gw3L9jd3uFweIBRAk8QZRTCo6UmLwqq2iGERahQyKkaS117tLRBhLmRuBLmpaRuYJxbqqah8Q0IR6wNXsbEPdfGR4E5pFVEkmQQ0hqA1ZovCPMtitrqthDYKiR/l+egMSlax0gd0aBCO3llVxosDnCf8Xz04iJJQ8QoI/h///kH/Mbfe5Gm0Ujn0ULzvXfeYWfrBW58rsuXX/xbXOt/Du1SMhPTOElVz1nMHhMbRT9+ieuuxi6mpItzaltwOjrkyZ277F2/xfUXXuHhk0NefvUVlJHks5rIf4PTvKLyhrPJhPtnJYO45no/496wYi2y5JXljV+4wWJuOZkuOHh4l/7omF//pX+HPzn6U6J+BHSY+yBIezPd4075IRFh/ku3BM2acAVFoOQEMoQM1vMAwuOsRDiBa4VnhbdI5bCtKLejDkUnaVEovJe4pfC2CzX8xhVonfFy90u8kv0yphzTWaR8/1v/Cw8fHTKazbl1+wZ/8Id/gDYx0/mUSVnz4MATRRXr3YSXr19FC0luHZNpzqyqubs/RUWKQRLxxZc7VOUci8KoGCcdWXeNxfkY30CSdWmOzhlrQ5MWvP7qFU7Gp5x/AMPbH1HLhMFgwJq7TlmeclY9DlNBiOfuY5/lEJf23+e3Sn368OISINoWzSSC6WzCvbsfcfDoAesbgzah1QgJRRHyHGPCfFnkM4rF7Af+vgDKerTWpBoyLeloQVdLOpEiMsFC2UtFjcK2AvQXWmAt+9dLKtcQ3HaeH0ddxBwXhSHchYulWBYi/WWGqW+fX5Wz2ogjxKfSiRbIaV/d1C3I355reVFMiZSgl0UY6bi2ucb2+hp53XA8HFH4Gm9iBCL8jk89Z4AIIM7JyQnj8ZiiaHNBGYrRy+t9ee38qaDbzxnPBZTagq179nWXWxKFa2N80TLs2wIuYlWo6ncSfF3wf/6z3yPrpMymYzqt4U/QYo148cXbxJ0MpOHgaIgQgtliwWCwznQxXxUbYhGYpcpDNzPkeYl3JbJuyOsSUXuUCWBbp5MGsXRCq7fWkkgarG8o0fTWeuzu7rG2WGOt16cTRxwdHCOKmp1+n6ay9LI0GCxYS2dni9l0wd0P7xClCXk+Yz6fI7Ui7mRk3Q6TyaTtwAkt8YtFwYE/4QtvvM7o9BTZE4gbNzBSkWUZa70+RgUnS5MoFmXB48cPeWV9l9oVmGSNxtWrvdkLnitIfLmw+rzxLNtPKfXJgv2PMX5iTZwluvuJZ53D2WBrtvz30sZbCIGXgjhJMNKRZl1qW3I6njAazXBWUJQNtsnBeKxxzGc1uczpZilJp0Nha9IstEBNpw5X1ERpgpEhcBMojBHB3lhJhmfnzGYLtATrPUmSoWNBUZU4JHlZUheLoMUhFAhFpCLm83l7swewQGuJa2qqtr+wrgNbZ5HPmExGWGvpdXqkrSCycw2mtdANcX2NlEE82AvoZh2apgZHYCR109b5Kqh5S9tg6yZUMI2mzEu8d8QmuFNJoxHOMp/PmYzGTM5D72ev18PVrSWgB9jh8ekAACAASURBVJ0IkjQjyzI6nQ61rS5V+1q01l9cK+9D0qmMDnoD3gc9mDIglx65SrbzarZiLF1OGj+roQArQnvDkq68VEIvFgvSGLSMUOqZHlPbBBqls9QOvGyYTwVx7UApnFcoLUGaFRA3n87I8xwhBFnaDUr+3tNYS5zEOOG5+9Edqrrg1gsvMkiT8FXWrkCXxoFRIiQ1zuKlQF1qrQn3To3UZvVvJSQWt2pJlLKl63vb0nQvnXchV1VoqUMLX2MdWIESKlhiCo9oHEoJmrKgKReU+YLJ+JxiXlDkIZluaodwAhNJTBxTNw3GSNKkg0fSOEttHVpHRKlHqFB9FGVD3fhWK0MQJCPaDdeHKrLWprXolNjardoflvTecOyhnUWJQFMObCvNUinnZ2p4QmZ0+Wd9ApRZ0nbFU48J8cODxh+0GfwgoAfCJhNaZ1oHIm/xiwU3r6whTo+YTU4wxQSqBdV0Ttbrs73Z5ejgkHo6o5ulKOEp5nNio1hMKhbzism8oBaKwgk2r2xgTM32xhr3vvEdToaOovIkWcretWtE2nDv43v0N9a59YVXub8oOH9yzOjgPjrJ0NtXWb/9Gp31beL16yjjiGPB2aOPYWMTKwxSrKPijKTTZ1YqlkLZ0oOzDdpbbD6jETXCW+KsQ7p5FV1P2e3vkHVTyuOCjgysrmVlqSzLi3vmkr7NiuLqP9mj/tT5fiYjvgzmCfHJ5z+roWiZOPKCsRhGCL6lBFq2hvduZUMr23YxKSRKaKSQeG9QyHCuUcF2G1aBpfcCS2CMOiexPmhelGVJFAd2nbWWplZsXN8j21rn1huvMc8XzOdzumWX+fScG9f32NjYwCI4OJlwOjwmykLRYT7PsY1g9/oVauuDYLWMiCKwtqKua4Rl1VZ6OUCvygrvBZ3egMdPDljf3MQ7xbvf/R4eTdIdMJ7PqaYzdna2OTwaMuhmREZSzYvWtjWALY31lLVFKI3C4YWiatrVSYQ1k0bS1I6yURRlzayyodCmFbEywdFDQOMdoqmCw+KlKjG0wE0L8vr2eop2r1Pqgg327F9A8cPeLqRuK6G+jcd8MJD/KeUqUmiccsQ64doLMU2jEcJQVTOEkRgVcX52xte++GWGkwmxOCQxA2ZzRxIlYb8jorRQzE9Yk3FoNfAK28DWxg3mOzPmkzlpt8Prb3yexWxOjCTvTLj3saEnFfPC8vLNbT5+eIiPFE1VkjcN66kmTi1v33nEv/tLX2bzyjW+/db3eOnmdfZGU67FL3AvP2Bv6wZxpWnSir7aAXenBdYUoVBzsXaEdgbFUhR9BQwTwFAhJBId3KfwQetGBEMLwiNIL6g9RGpZUw4uVAtZgM35za3/mMjHFNUTskmHo8ff5+xkSN001N5xejJEKkVZ1hxNcg5Gnt31CFsblIfzWREAX+FY2B7398/opoqNTmAdHx2f0s1Sev0UicVEXVIRIeaKbDNCyAa5kSK6GdOh5+r6Lv/gNzP+6//+99n6hS2SNUNlBZGu6Sc7DPNHKKERrQD4/19GADfg7OyMk6MD6rrEGMNisQAaYt0J2pNleFx42rYy9wMLoMt9SSmFtw3SNSjh0Qoipej1OqRpSpRkSBOBkJcsMORqP6pdsEuGi8LEDxrPsm+W4P+nFZEa74Km3Kd9XvumZS7o3NKtNZiHKCGJjSGJNDhNmkR0oohIPT9NXYJNnzxfLdu81SmFy3vg0oVyud+zKpj9NMazKbR/pqjnfcvV86H6uny5eOZ1ogXrguuZfSpG9m2rv5AeiWNRlhwdHbK5EQr9i8UCZaLQoREHcxgTx0jC/JzP5yRRROPqVbFLCEEUqSAgbBtiEyERlIsaKS2xTqmaEUIQcnHrURFo5YmNwWuNElBVBUVVYa3HNVUonLiGbpZy3uonSSHIZ1MGW2uYOOJseIpSir2dXfYPD/AEkxolwLdC4ZE2GKWDYLizxMvzWYdco2lBKxMJhBYkSUTtKmaT0Gqvhaafdpi3gGtdflKn6NMcpZ66Jj/CvApFfZ6zBvxoi+OPr4nz1A98/vPaSJrWMWT5WBRFmCTmxo1rJP0u692ESN3mu299k/39Q85PKxKdBubKYk6UGJIoo5sm4ARSCeI04nw8RpmEwWCb2lWYVJGmKZPJhIODcEGjOEVGEYNen9lizoNHD9Fa0+uldLMeKtEoFRFpyWQ2Zz6a0el0yHrdlfL7yckJ/X5/ZcNtjGE4HAIF3W4fh+f4+Jjx+BwpNdJcLLRCCOI4DYGklcRx3KLAQZjLNpYkySiqIKqcJClpHICW2WLObDKlyHOyLKNpbQvX1+IgsqsVaZwhUZwOhwyHQ548fMLD+w8QSrG5tk5TlKHCHHucCyJMV69eRSnF4UGwUF9ej7KuqIoSoxRprxdEFZMuUkrSOAARk9mUk9Mhs/0nmChhc3MLpSPG52MWiwIdJTgr+Cyjw1WwKzxKGJa2nzoyIDVl7RC+oZOqVTAVRRFYWHhPVebMxwvmjQutR8owWN8i6wzoDTZI0ow4jul0+mEhm+V8fPdDxuMxO1vbXLtxlel0ykcffYTzGf21PsPTA87PhuxsbvHVr/0ycRwDOlQcZKgeVE3r1uBaRyYNWhq8tygVhDHryuJkSEBiqdE6bMRVVaC0AK9XDCkIieOyumCtw3vJoijxLvQfR1rjqqDvodtzZ6sZB08eUuRTxucjTvYPOT4+plrkWOtpKstgMMDEGU0DRIbYxOg4AI3GxEQ6Bg1xlmKiFO8E03lFlucI66gWc+bjcxwWJcWK6t/LgpV9WdRMpnPqpsSpcLxRrKEFrSCCZeuAa6GuZUbzMzV8oENeTvSVWLFwoK2iXKL0Lh+Tl0Dxpz7xhzBxnL0E/D1PvLj9k3XYRB2BPaBxfDkdsf+NP6cZnzIZjymLmkwFwcOHBw95/KTm5naXfpYxmyxIRE0/1SwWNfdOZ5yNa+ZSsUAyyWt+/ude4I03bnJ8/yNefu3LfOf9r/Orv/Fvk5cFCMFoNGHnxg2KxZyzj++SJAmbL79A2v0VnJRs37pF1YLZs0ffwg2PEfmCyWLMI+nZG/QQG1fp794gWd8jb15nMb+LlODKhrqq6KWGUmeUuiYdnpMMDIu//RWmf/RP2JKeX/z8L/JH+0cgPHkR3AYhgL5LVs7yfC4D68sVlWUx4vLrQhAqn7p2TzOuHIE49llTH/xKVFUShClFCwqIy/OxBfh8I3Beh6qQN0gvkV6ivEGhQkLQeJwUCCWCzTHte/E4C4tW7M/5piXNC+p6yHQ+RrJOkm0jVcrUedY3dxifjygXlp3BLvMoVAQX8ym7u7t8tdPnyf4BVVPw3XffY31zm0Ves7V5hYOjU67fuIkQoXo2nR6xWEyxVUU5K5AyrI3dbpeyLFksFvTWNxmsJRyOC+K1Xe48Oeatv3iLtJMRd9b48298m14aMz4aUixmXN/b4LWXrtHtJKx3YiKT4pylyAusdZQ2pELSe4SCyivqKuhgKQ80UCwaitKSl47xosELSFSEUQIRG+I0W82twKJstSjaxU3oi1jCe9+2I1xU5petYsv5tnpMtUYQSYc4yyjnMY0tVqLNjW9CQvAZDUGbQKkoaBTEEQ8+2uelL9zEChA0GKN5eG+f9SubRImkpkRhuXv+DpnscXXwKvP5QxIf4+eO3sZeAHTygvzoEb6YUU6mzM5PGJ+eM54VyEnE8ePHEGnm85w79x5gsphfePUa/89ffpv3D4f85q/8PEf7B1gl6MwKHgxLXryyRq+r+Obb71Dk3+Q/+8//C44PT/jT3/vH/OZ/8l+yM+7x+PycG52b+Bpyf8xvbP4HfDj6PkP9GFHXOHGJci9DK/FStDb0BDuE10hp25ZogW7vP2RrBNC2InopEd6AdDgfGK4SizQJ/2Djd+irWwzn72MKRXP/PncPcnwcMS7DerW73uUv/+otnIBRVXI2hV4aM80dX7qxxv75gv39EVsbGufg9dvr1HYdrTyRCsWUTmcnOK0KTVGV1EWBdSVrWxuMpiOSbodIrNE0DrNbkyfnfO/NJ/z93/4if/V7j7j5FcfW7YbCLpguTklNRO3sihn80xzPsm9+smQ+XGtrHco7PrzzPh/d+RDvasqyxtYNaSejqQIbxHu7YuLUZRUIjZ8S0yzvbWhbJW2NwdKNDf1OzKCfEWsT9jCpqHxgw0TiaReqpby20k8Xbj9xJMv94RI76XnJqH8OE2cFLEuBcDLo/HmBbdk44YdccitujT6UNVgRCtakMd0kRuPpZw2dLCNLY7I0RQnFtCxX7V3iU7Lopgl6ko1v2N/f5+DggDzPW/Fo9VTBZikncTnW+mkO0QJkSi7Z+H7VAhekPC6Eri+PALbRMthpgWAZwOAW3IiUZDI54/3vfg/nG06OnlDnJToyZCbIbtTTCVprrmxu8+jRI2aLOcfDITtbW+wfHnPtyh6VDXIka4MBSZQynpyy1h+QDtaZTCbYKnRwlE1Jf32DeZETG0O/16eYFyspkszEweW2KfHWMm+/++hwSKfTIY1SeklGFEVMhyNcVTPN5+xsrFOWJZPZgvX1AWVZI3xJv9thUdVMZlPiXgcVR5TTeWBtac1g0KPOC6bTKYmJODzaZ2dnB1l5ru7t8cG771Itcq7tXSWKDS+++CKPHj3ia1KCL2mqmuCvsmxtZHV/fdrceR6r6vJrV/GiFM/RQP3R5syPBeJ8shr5yW8JgnsXaGtjK7yT1M4SAxsbG8RZioxi+pvbdNfW8UdDzqdDdvoRRsd0ugNMZlBxTDEc44UkjhVJnGHikrwsyA8PqaucJI1aEUrB2WgcHADUnG5/QFGVpJ0u+f4BmYck6VLXwYYz63RI0ojFYY4TUBQVSRZEj8/GI27fvs1isWB7e5vJJLhGRVHrxhPHNFWNdZbYJHT6PTqdDkmS4HxoXTEm9NohHFmWtQn9BeV+OhuvBI/X19dbdoKizitmkxlFOcfZcBvu7e1S1AWLeU5tFFGUUJYlD+8/YDKZcHx8TC/roiLDdDxhNp5grWWwtU6/P8Bay3g8DkGfuJg4BkkkFaK1RbfWtu5aerXARZFmrT8AL7lXPGQ0mbG1e51Hj55w9+P7NE1DlmR4JWjKT0yHv9khgwq9x1K3gXUn67K+uYG3NaLKkTIwoRQhIVnqDSgU0ig62lDZhrqxVEWBNinahMWvKErG4/3Q6lHXbG1sYozhbHjM7pUt+r0ucRzx6PF9euMO27s7LIqCf/ln/4LNzU1u3HiBbn+Dqm6w3l3oFXiJkDEQbJSb0uL9cgP14GsiofEEpNo1Ei0VzjfEIkVIQW4LhFsejwiCy62IuPj/qHuzJ8uO+87vk5lnvXvt1XsDDYAACC4gSIrU4tHIskaj8Yxsj21pHOEJh+fBdvgv8KMj5PCLQw8OR/jFdtieibDsGY8lxYwUGms4JLVRpCguIIAGutFrVddedz17Ln7Ic6uqAYgmRRLUZERH37rV1d3n5jmZv/z+votoCNIUtF+8HTVSCYxpCAIBgeCd229w/53byDY2tMhmlAufSpbEPaIQotAnBgRhQhJ3W2BF0OsNPLU36qICibPePK2pHXHsN2fb1JiqRAQC2TiUE8StyXa3l4JRHB2dkGUZKjCoQDIY9VlbWwEhCKLAM4eWrAGWOVV/9YZzDt0aTp6/5+fb2KVprkA5jTCyNa5rq7ZlYS6CM2ajsR7oO2PrOHxnRfjCyDp3dhC7CN5IKdFlAUCkAowFXReYIKYxjhthwe3f/4f8Yd6QAJujEYvxlEbDQhQ83D1BSF9cjscVeS5ZX5H0OgLXVKjAYVTAsZkwnQu6q+vMmjk3LyXUp2OS/hpf+N0v8Jmf+iS9Xp+4O6AymlQJ5jtvEPf6ZKs3EMNr9IYbxJ2rBJFkWuwzCAsC5pze+Q73jm7T762xtfUJRpdXSQqHrU6on9ymE8X0N28yniSsHL/OTvoscX2PSq4hVeg/J7PANAFJ1OX3/uB1fu6ZN9n6W7cgjiknh4iwg8lzOkkXKQKyskIFF+8uHxvtMz0ExrhWhviBIjYuJnk4PLNl6Wni5/CHd699b8N3pSQOIQKEUVhlWibYOQDOUl5pHcq1cdXKMzWcDUBGWCu8trz1gpORIRI+Fal20KAxzmCMwDlNbRcYapCCVG1hyVBhgxAxTSOIpOD44S5He09IlOP1N75JZhWJclSLnDRKOT0Zc/3aszx48IDHDx6QzRdcu3aNL3/j22RG8vf/i/+StUFClU89W1A76smU2XTGxuYmjw9OyRvH17/xOlZ2+exrI65f2yBVDW+/c5e9ozGFDHiy+4S9x4+wVQbGYqUhjkPCbsz6zBB1ukxyTRhIkihkUU0QIvIHcwQIhRSKOLBYZ7BaUFQeAG+spDKG2tqzmsA5R4FDCkmtIjpKIhWARBnn2RrKIkNJKKOzbrkxxktu2htJ4tdTUVlfnIcKYwJE6GjikE53QF3XJIMB87nAlgJnG58yo82Hy34QDhGoltGlmJ0sODk+ZfPGuk/uUoKyrLhy7SoCx6dufpbcFhRBg60gDHuUZkpXrBKmI8p6F1HlNFqjhMKEEfWsYvfBmxwenRKoDqeLAq0zur0+otJUTUN/qFhkC770jT8nimL+o7/xKt+4+4gXnrnBoBPSTU554/GYO09O+fTNAQstGfa7/KP/7X/lEy++iHOSnW/8K0abm7iOZvHOmO7NSwyaDovwkFf7L/MvZiUqWIDJcXg/HIc3l7WuTZwSDuUCGuktxLG+uSKkwtjKSyHOH2PfwJC+CVKbgrXwKpHq8EzyEnHdYWfyVQYmxh094fbXvsHWrU/wzltvMhitUGQT3n5nl8d7T3g8qzFNgHaOLg2fubXKo6lla2OAUhlfuTvntedXuf94j1u3blJllsP5jMPDBSsrK4wr6EUOXTVU2ZRuHFLrhuc3PsHKyhVUfwCBoul2mUzv8LHPbvGV337E8emE8Rci/p2P98EMKIoxtfYBIX45/XCpOOdrsnvq66f+jGsPyix9nDgDDhAC2TLZjay9gatrw9JNTZ4tmJ4ckk3HlPMcEUcMh95iYXfvwAcFjFZxgWSxyChMQwN0jOdeGQmNVB4ItJagTeoMcAzSmKtByfVRzOZKh0sbaySdLulgDRWFiCAm9HniBNIbz2M9Q8Y5RxhF55Im6RmT7RW31y3OmTROI5csENvKObnwWTlasMZdaCj6r/0uuJQ7WaTjjDkrHJTKgRKYqvYhMMo3+ISDpvCm+qptOpGOqIM5OlgQBoKuyTC6xlmBdh2cLFvwW57NqxLtPiwte9MnfOfe6+zffkSwPWXYjYkSi0w7NAKK0mGaClnmUOY/vBvtexgOD5a9dwgpz99feqa1PghnzJ0lJvae+9nh2jRJD57jvKROyYa8KJkdnfKVr/wJsZLMsgKjG6IkptvvYYwh7XbpdHocHh7z8NEOxjZ0+yN045hO52xsbOFo2mZ/jJSObtpha3uD0EAxmRCHkroRDHo9emmHJEkYrY1YLBY0VUkgh4RKEai2qSQDinmJazSRDAj7Xaq8wFQl46NDVBIRJiGdTkJfdb3cSQV0IkVW1T6tz1q0NTiMT4nTGqQg6XUwTePPdkGAjGN2Hj7i059+jTvvvMP66hqq4+WIKg6IZMDGxhph6D+Th7sH1GVOFMWYQNGY89CEM4D0L2DRLX10ls0W4Ey5snR2uqhi+VCMjb8npNJ6syopWy2nFWjniFpZUreVDTlnEdYyWltnbXPGg3uHZHnpTaxwUFtkDI11OF174CPPmecZEGF0gXWGoirJi6qlHku0ranLgqIozgwWoyhCxTGN8YbCEYlHjVFIEdBJvdHyIs98t7U1vwwC3x1ZUu6jKKLT8XKmsqy9aXAL0MjWUIyWyCiEODPLjOP4qa8BrIWmOTdh9BHWPuJbtX4EqiW3NE3D+HhMnmX0+n20PiLPc4qiAMNZipQxlunRCWVZIvHIXr/fP6MunsdlG6zNn6I1+vQDSRRH2JaSVpQlxijStMvKyhon0xl1G+M6mUy8W/7ZTfhj8CpxEtFSzz3qDyIIiZKUIIiwpgF8epK/d73hY6QibGTpGk1hfLdAY3BGY5uCKs+xUmO0oyw9hbBpGg9AxjFOCJ48ecLKygqDwYCTU59UNl34Q8Vs5jjYf0I37ZF2+5jGYCU42x7ERSt3QCBc6KUuxi/oRi+7bT45B+tp1054I2XhLM66s86e94BQrU+FIWhNLXXVUNcl2tTM6oZICbCWg+kppql58vgBVZ0RBIrZfAzOoAKQNvDeFShEqLCC1oBYYawhFMEFgLbB2zR6+aRPqmqlh21nMWi3cyEESeJBr0BFuGVChdOoFtDodBLSNMWy7AB5UEu0yl7fxfmr54mzHBdZGBeUYefMGM7JOh/c2bq4KSxX9KeZOO/9mafYOdobtysU2nhWFsMeZj7n0qjL7X/5ezCv6HVTRt0Op+MZ40VJ2l+lXEy9l4iBeZZzZXuD4+NDRv0esYrIM0tT19SVJi8q8kqiyoqyrNjc8J2R3soqYSA5Oh6jDXRR5HlO3lQMg4TJomDkJGvDERZBUR8QaUFSz1nsPaZczBAy5NOf/hzzrMQiqeoFuhCIxifspOUMHU6J4xAZRkTS+7VYnaOiIVIKcAZrNSpwXH3hZfan7/B8OeOVF5/h4H7Ig4NDoijyPkG6JlRgWsrx0x3G73+8j3L7XWjyP6px8V/0953zRoJLOvZZIe5ok8TPDzXOv+8JA+5MMnUunWqZccJ3BwVLBuZFEEzirEDJIVUhmakS4WaEkWRtZe1Myvb48WPKxZS0v0qgfJrj6Swjqy1bW4K3375Dt9tlMZtz//597t17xNaN54jDhKZpSDs+ITGfjckqgwhiSg3fev3brF66xNHxKaVe0PuFS5B02du5R9lorDE8vPcOTVUTSEfWNN7IP4CiyLhbVZzsP6HX6fL5117k0qUtGl3T7w85OT5EyOQpRvKyULMCdH1OcQ8k6JYhI6QHo6UMcFIhZIAIEmTQrqcf5DK6nM9WpnHxXvogKZWUEhUEhGFIGEeebRuGGCOxTiKFQ5oPlkL8SIeTLC1Iy6ImEBKnJEr4mGGrDf1eD9lI9rP7XIpvkLiQftJHmJJEJMRJn1QGpN0rhM0Cmx9SaoEKUrCOPJsym445mBywc1wQRhGjoSNN/R40n4OuLCERx/Mpv/uVr/Mrv/DX+O0v/BmfeekKa+sDzLsPCMOIotHoRjPsBJxmFe/ef8wrn3iBw/0dnGkYvbDFSVWwmvkudolm7OZ8JHqWY33KibqDIkA6iQ+Wb9lIeLNihz5n+C0/orY+OU/saZ8v4dBO02WVhgnPxy+SyE1Wy1WMnqMqiViMmR3tczieUd73TEd0w/2dfQ5PJ8xLTd04cJZnLw95dnudURKh7THj+Yyv3F/wS5/YZrUb8ObjY7709Tc5XliaxtFLI94dP2GrA0Xt2F6NWB8maK3ZWl0ntJCPnxBEx0RBn2GzyUbv43SjEUef+hJ/9Mfv4MKCSD2La0IalVOZ3NcKP0ZG7Xf1rGiBbl9ztO/5Hzo7SC/tIpZ7tcAfyKqqYjGdeeNXJRAtENs0zXnCofBy3uU6KIRfas8PcW09vWTsWo96JWHEqOdBodFgyHA4pN+y55eNouWhH3yakw/DVGfXcUaIcRcbTh/Mtvle5SDf7zirkVo5k7BtA/Oiqbt6mnXy3v/LssksxftrqeWft9ayWCw4Ojri5HifUaIJbYytLEG5wIiArGqw2hA0JcboH/q1frchEE81/i7+/z/ove/G6jjbj9RFUODifq+QwGLhm/5K+fNhmMQYp8+ABtOeKU7HU4yxSOn3E91o4iTxkeGVX5OiKGqb0oZeEGOkBmcJwhgZhgySFaywlHWFUPKska2EwGhHKJy3/JCKQCpMozGBYdTtU+DldYvcN1iUUu1DUuOWhu/Gy/eE81YnVvp9BgW6MZS1JbANtmkIliELYeAZPKMR/X4fKSWj0ejs76/r3J+5nCOJEsoqZzaZsnbpCkJIau2emovvtb5bBuKc3cvtvPwwnq8fIJ3qg4cMFDivv5S2IYxjEqXo9FLW1lZ4+eWX6XdTGl2SzWpUFLO+scna5gaP7+z4g7TyMXC9UZ/N1TUSGaC1ZTqfMp3MqBuHbhwqDEjSmLysCOOYtfVVJpMJdV1z++5dRgO/yF27cd1Hih0e4pxjMBoync5b1oJic3udosi5d+8+3UGPOIkpy5Lt7W3yfOFlOECe51ze3qbT6TCdTpFBj9Fw6KmRQF1XBEHo07UC6aPxjGmTObwZ1JKq7+Ux9dn3xuMxs9nUI+ZtCpRBU2U5jx9n7O7uYa1lMMiYz+dobVhd9bKoS5cus5hMGM/HOGHYvLLpC7kLv05PT9HaMhh2qMuM2axgMZ0xGA0JgoDhcHhm6In1xopFUXBysiCOUzY2rnD10jU2VjdZNA07O7vkhdcL1rWPdv1wq0PvySSl37C0thgHcRIy6I+Yp1NOTk/opSFBoAiDDlHgXfLL2EvT6rLClCXGNDRljTWSqmoYT3KMCtGNxVpfoPd6PXZ2dtoH0PK1r32NOI755KuvcuXSZU7HB0yPjj1DpdPljbde5/T0BOM01248QxzHWCupygohQpwE7Vq/JRGhkQROYkSDkg5MDaYhiUOiIMTqmsV8xtHREdbC2tqK93FyDgigNaVuGkNjDG+//TY7+zsILE1d0E1iyiLj29/8OoGAz376k2yub5DnOYvFhCybe5AvjiBQSOVd6YMgJs9rTiYnaGu4fvUGZdTxZnS2xIlzaj8iptPpAnhJXxJjqwSjaoQVdJKUMI6Q0hsgCyuQoaQ2NdJaVtZXGaz02q5xy1wzeK2vNIg21eKv3HBciKluo0E5L4zOFm6lvFm1Fb5Le+F7T20G79kYLsrmlofoD3ytNU6EVPY8Jc/OpqwUB9z9468ye+c2m6N1Lq/EPDnYUVc3CQAAIABJREFUZ/80Z+e04P7tO3z0cof+YMS79x9x7do1vvKt26SJ5calLiarmZ4W7O2fcDhPmNcBs9xgg4ailvRSiQwkoVWUkxNeeuU14jikyCdoUyMag+2tce3SZeiuUOcFUQzM7jI5ndIcjLl0/Qqj7cvsDzfJzRQRx5iiYS0vmMg+/X6H44PHlCe7nqUWjxiT0u+FNGxg57uQpMggoSkLjIWwY3jhr/3bfOmf/VOuPf4Wn/nk3+bb0YvsHJ1Qi6b1aVmQpjG6ufC5O99J9KCFaNe1703bfPH3H9sQ5wwtD9wITwN+T8Hg7x3fUFh2jZYpJv6Y0jYkJCDc2VprbOXlocp3vozRIBrAeMaJkTghmeWH7B89omlC1kdXSRPfvPnOG9+mzjPSAEajERtdyeP9I8q64cHjR9zfO+T44JRvfeObCKX4yAsv8c6jO9x7fMCTk4xH9x/w3K2b1JVlsLKKCiIOJxnrm6t8+ctf5v/6rd8iTvuEcYfnP/IZ/uDb90niHdYGHSZZwVe/+sfcurxBXddMZjOUtKTdhPligjOCSjfIaER3ZUThHMezGc9ev4kuC4TsoWJfNi1BE+kcoZQYrRHW0CjfnW+sIQGsDUBYwlBCmKKiLmFvSDLaIAxDojQ5awKdHXBalo+fPz8Py/lbzusyAVNbdyYJTNOU3qCPRVMVa6TzVWRcUmcFEg1Kv3d5+dEOIUC1qW7CgGqQkU9wQjjqqiEdhjwZ36NaWC531jjUT9DWsBqsE0YJg/Aqu6d3iJKYsCi5IleRqkdwsk892UXamjRdo9fVHM2OeeG5G6z0e7z94AGP78+JA8E0L5HKEZiQ65sJUez4jf/3T/nP/8O/zf0nO1xNIuxnPs6b9x5yNM7oxAFPJhpJxaMj+ERVsHM0JhCWfuToJgEP3vk2r7z4SWS+wp7a42p8lcvRNf5V9SaKDk7U4AIQnmnr25Ma2TZgNBYrTJvi5sApnwJHgBWeOReicG7KrKn4T7Z+ncnem0zDU9L+Jof3vkNn/TpP7tzh5GCfja2rFK3nwzfffJNv3n1EbQJWel1+8cXrDHsxeX7KbFHwZ7vHHE5zlLP8vc9f5939U3aOBOMKjFbc2OiwM20odU1IQxqvcG0jop9aumHKcJCipKRZzFjtbLFitzC1IkglJtsna8Z8+iO3eOvzG3ztzw75yu+/zac+/TF66SaL6p4//PEhs8LObsmn9+X3DtceskQL2gA4684ki65lwgoRgPCsbmUM2mhOD/d4+OA+s+mYJArRoWfbL88lURSRdjscj0/JsuzM09BKMKLNKRPAMsq7qugoyVq/y6sfeYGfeP4Kt555lsuXNhj0uqRJjAgCCFRrguYpToaWKRMolklRVqjW+6xl/F7Y0wRPAwXOLcF78TQ+/0MY6swYVrT1kjfSF65N0z0zpi/OzkumDcs5j0D3f9dFL86L4M3yvFWWFQcHBxze+yZbwQ3kIkBHFoIApwLyyiJxxKbGNMUP90L//4bgA0EceBqcEUI8BRq+V/r31N/RBqlYb4YISLAGKQVKCW6/9SbHh/voPEObhjhMWSZFIyX5PCcrC+aLnCiKKKqGxdybcUexomqToQUwm80IQkmTl7jthm7c4ZlrV0FJFnnDxvYG06wkKxaEYYQajkiimLosKYoMYZPW58nR7/ZIgpBIBpi6IQlCZOqZL9pZppmv0+I4xGhDEChcknjTPykJaw80BUpS0SagoVFKglVEyquAVvoDxqfHXL58mVdffZWdhw/opT3u3b2DrRoWxYJer0OnN8A4Q7/f583bb/KZtXVU61l70TtxOScfOL0XpOvvTUeT+POfl2UtHdD+cuMHAnHOH/jzzPOmPcyESiBlSNpNGA4GvPjiC2xd2mZ7exOBIXSOKIg5Ph0zXcy5fPUy+Twnz0oaa6iakuPJKeurq/SHHR+THSVc2u7x1p07HOzuoeKQW7duEccxaRzT7XZBSqIw5u79eyghuXHzWaaz8RnybYxhNFzj7t27GAdrKytMJhOqquLSlStYZzCmoT8coK1PcdrY2Dj74KMg9GbITcOoZdgsGSmqG54Vz8ZojKnb1825ObK1bRqFI45jkiQmCFT7kCkWi4X3yYkVx8fH5HnJymDIYjEjCkN03XhtZCA4OjryD5QQaF0jJTz73C3iKCBbFIzHYw6PjzDGcHRwCMBlveWjYsE7xmtLEHkUcnI6bjcWc7ZZhe0m9Hh3h/X1TbYuXeH47Xc4PT2hKEqUirB4MOJDHe0mI6RHmGUIPg0mYDAYsBgOmRzEyCgkChRWW3AlAksaxAQx2I5/dKqqYp6VjI9PaAzUSMAX1nGSEoYxuvYHlDiNSJKYtNOhrmv29vaIQv9gdhJviF1VBYvTY/abijdDgUOzurrKxvomJjKgnTcndJIoSACBcgqtDXEAxjQUpZc2lQvBfHrCeHLKnTt3WMzmRElMmqZcuXKFXq/H0dERp6fH5FnlC+O65smTJ0wmE5qmom5yKl1SFRmbGys8/8JHuHL9CjsPd5iOJ5xOFkymOUKFJEGHIq9BKqLYEgYh1kAcx3SUQsUJp6enBELSHfQZdPsIpZAqRomA2tRMJ3PqMqecT4kEJGlCrDrI0BcUxmjqWmOtj2etqpo0CBgNeoShQgnvHYW1HvQ46/b/lTPD8cM5dFOdLfBS+vQHi1+grbzQMQdvfrqkF/8FIM5STvVe76OL/izvBXECqSi0RRsHtiKiob/zBm9+7cukgcQFliNp6C0KpALT5FhbkgQhDZKsKhGB4uHeAaelY7MbURvJ4cGUkyplNwvYny4oRYALJTUQpQlWaEY3X2b3W3e5dOkK9+7cZTjqsX1plSTZIF84SLvcubfL5Rsd1LzgYHJEp27Y3L6Eeukmutej6HS4/pEr6Lff4OjgkN/8p/+Ez6/2KG5uc3NjwOJ4TJQcs9JboRvGLPqXfAdn7Tma43d9HatA66aVtxTEScB/8J/+Z+y/+QWO3/o6H3/xJzh54Spf+dPXPTtMQlPkWMKnOn52mbzUOlS0zc2/cLx3Hp9Ovfjwx0VASUmFUu59xd/yenzB7l9I6aUvoVRI0XaOVYhUHohAQW1qL/twFiMcVuqzCE8lLUZ4eXPm9nHRVaL0smdhZDWHh4e88MJzZNMpIQ3dOObhm9/i7oNdXn93l/7KqveLs5b1y5e49dwL9HoDrn/kBX7tl/89+r0BYRsvXpYlZVEwWtkk6j7hv/5vfo39vV2yvAShmE6nzOdf5It/+EW2t7e5fu0KSik++tKLrPX71HVNefc2+cET5vM5R4f7CCGIw4jZPGPveMyT/Sf89Oc/y4O9QwZxwOalbYqqPCvIlvp+IS2m0UinaUIvZyicppHgnEJISRArZBoTJRFxlJKkXaIoRsVRSyv3c6bwklSlgjNWBlzwufqA1Dsppe+mRpo4TUmtJu33CXtDrBjTNA7hrOdN/kWOjD/C4ZxDouj3+xwdnOKkoCktThQ0xjJa67EQM4S01LbCaYlMLNvuKv1wyNbWBlWdkwQN9dG7mCzDVAtOjx5TTKbsH59Q5A0fe/FlHj58yOOHT2imGXmumRpNaUBaxaBvyPSArc1VLq8F/J+/8/t87NmrrD33Gt30iBevXeG//8e/w4vXNnlnb8xL25tM64p/8dU/49bWFk92S9T+Djde+SQqKqjGOcIJgvWQoe6jS8UgegYnJVpkWCYYa71/Uru2GAcgCQTodp0XSKwCa2sUtk0Qc3TlGrmxfCz9WcrZjDyskbJHc3iPOrOI+gmPHu5ycHDAxnWYz0oe7u1zZ+eAz370eTpRSJoqqrygrgU7J2O0NtzYXufmRg9rao6mU/JFgUYgrWUQeRnwZkfT1LA+WCeIHUEomWU1c3tIVndZqTpsbG5jasPB7iP6GxuEZUAWFZTlKbaCX/ylv87f+tyQ/+q//Z+49ZPv0is3PCtMei8oPsQ1UvC0zwt88AHMLmm0rSzHgznghMMK68ERBE4EZ/uC0TWnJwfs7+2SZ1MkhkZXzGsfgDKfz5Ey8EBkGFIUBVmet4amUEu/HC+ZMgiLMI6uFLz64gs8d+MGP/czP8P1lZStrS3SNCQIlZeaBwqUxAhAtQoIVMv4ky0o5IEpizgD+ZeXLs5ftLVHC978iKbGudb/wwqEbb1VXVvz4FO2nFR+fW4b4dZaGtOw7DoIoXxjtFVNXPSrW15T02icw+8TsyfoIqUy0O0q4jShqaErAs+i0lnr/vbhjg+qESzn6YT+tWr3AD/eywI9Y944B43DSuvBY9mmbDoH2hJJwZ3bb7OYTdGtRUReVwyHIyazHIthcjoly3OcAxl5z9TlvkRjcRbiOKWpak4OT+h0EwJ8yItUsLmxRr83JGs0+wcH7Dx+iJOC3soq+WKBtdaH/zjQdYO0hjAIiJyjF0V0k5QkCHDOywA7/dgnQurKR6KXOU3TEIYxnaRLXZWEArpxQmMW3prSWJoqJ0wSQimpypp5psnyOT//cz/Lb/7f/5Q33nyTldGIN96YUpY5R4cHqEiRkpL2unQCxZPDI7bWRjy8+zYvvvIK/Y0OQsn3zdl388OxOLQ9Z+AJKUAK9IX92/yAR+e/FIjzXmrbxa+XHSEpAqS0WOO9Im4++wxXL1/x0iYEKo6plCJsfzkB/eEKUVKxyDOqrCIUMc4Zam0ItcbVBiugqTVCSJyFsqi8OaUKKYsK23jKUq+TkiQxQjqKovBJGZGXCiWJl1PV2vjEBGsxzrHa74NwlGVJmqZn0bNpmp4ZxwZRSFMURK2/ydJLxoM8S1ZIQ9PUvoBuWQoXH7bl5yXbw53v0nkD5bqu0abG4hOiwnB5cPVdvzj2B/i6rjk4OiTPc9I0JUkigiBmNBpRVQVVU1LrhgB//cZZAqmoG4Oynk6pAkVRVx7JTdIz1k5d160Bm2N9fRWlQvYOJsyzgmFT01TV+yhly87/hzU8vVWdvT4Xo4AMA4IoJAxjL9VxnsqndY3TBikDgtCRpn4xamRDL02oaoupHaa2CCxRIOn3+8RRl06nQ7/fJ0kDLzUrijN5Wt3kNEWJbgqc8WafZbGgk0Y0dcHt73yDzUvbJLGgN1rDKAu1AWkoi6aV5kUYDXV9TFkumE69v5PRDaenp0wmpzx++JA4CrAmYXJySLGY0uv1ODg44PRkctadLcuSuqq4sr2BtZb7D+/RFDlJrHjm5i0uXblMUVRMF3PKQiNEwGDk2VudtM9snvlOYSOppCZOumwOh8RRiopSjo9PsdYSxBFq4KmIBm+gW5Ql+WxOni9o8gUyFgQiIIi9dMA5R103Z/Rih0bIZVqCPUtqEABi6RHULojWnHWV/ioNh8Naf//7CHm/CfmuEcgLeu0WfXwfhfliLbn0UbkolXr6ex9seGyMwxj/XkyD1Bl7j77O+nqPpjA0lSbOM8ogJU4SttZX6fdWOf7KHSoT0gsjtLYUTUNea7ROWOQVC6V5fDplXDpK01AahwtiRODXtNH6KnSHQEwYRjx/5QZa18xmU4Ig5mRiCBtNFCUIYxmf7iFdSdY49k+PSeKQYZjgijnf/saX6c8mPPOxj7L13IscHdxnTTp0WXsfKxFQ5CWdbkWQbGDqAtFZRcgInMFpgzaGwEpMXSCtIGCA2nqe4PAB9vgeo5UuQaSoCo1VXujhgmWehzjzuLkocftu470HgffK3H5cY7lGK6WQF/quT/dfvRni0vtBtjRmeeFJW0oZl5HkS729cd4LhgsHHYlDuBrhBIoNBt0r9DqecRIHCit6rI/6FJ0O+4/v8e7OI46Op4wXJU5FfPTjn2A6HfPcR17iaDrl7/+Df8CdO3d46aMf5eWXX/H+dE6QdruUZUk+ztk7OmZ1dZWD/R3KfIrRPuUvVAZhC0YS7HyPu985pDMY8sorL9Nfv0y/1+Hy9WsMRz2Ojw/ZXx2RZRnHx8dM5jOysmB9/RUm84LhcIVuv0ejS6IoOesIL+sCITUaRxMIrGnjcgNfRCvjGaNRFCACSawkgVw2EX1i53JInt5XL4K/38tYdrLDKCKKU4I4oiljH3EsalTwweyDH/UwOAIg6npWswgUxuSEkUJLQxAkNO6EKIxZjS6xEoz42Pq/RWQCSjIi26UTJ2iXIcIVGqnJ8gOqqqIyhs3tq4xGa8RRlyvblzg4PebPv/0Gh/k+pfa+VpUTNFNNENT0TjM++dw2Zqfmi19/g+dvXicKJcMg5N/4+Avs7h8wTCQn4xmdXpdFVtFJUrIq8zIZY0hTQZXPSbsd0iaiFhmuUoRJD6ETymiMbTROtt1ya/Byau+L59kezh+4hU/MAc7mW7oAiWIleJYNeRNdLCiqQ6wTlHNHneeowHB0dASBIpvlHB1PeefBE37uJ15DNzWBtFSVRgcx+WzOxmCNOPbAY1laxuMFk0VO7QRZKdhcETyzsUKSRjTaUhrBwWRGkQl2pzUqLllPLTKbk4SOcjFloS0rNy/hlGAcLpiLnFAHrHYvsZftkddz/sZrL5PEY9IgJaeLETNwP569/PuRCb0X7FmCqEIIjBQedbF+3ykWc4rF3MvYtfbrk/aeVo0xxJEHcKrK+3ouzfWlxIdc+O0HnJdjC2DY7fDCzZs8/8wNLq+v0ot94EgQCM9GkM4vJMqbaUspEdLXIEK0a4tt9zKpsC0Lx+GQ0i4vkqXEYykbW8q/4YdPlpItkLRkOknR6snOzkPnbIeLILWwLRDVBl345GB11sw6Zyqeg1TO+SbtdDqmqgpiqcBG0CY4haECq9tUuB/yhX4Pw32APcB7+0WWZZflA37+Qo3hw10MymqcDNCtjE6gAUtgNLOTQyKpqFWPWksQGUESM1/kWOvQWiJFTNwZUDQFQlo0iijs0pUCU1qQoWfhCkvT1gCdXoo0DYkLGfYSUmvY3VkgZY2QAZPpEVXtQds8z4nChMU8oxdJZO0YrW8QOk2oKzpxSBqHWKmYFzmiyBhFAdLkgKObRNimwmnP6BRVSRpIGhGQm5oUxVzXRE4ha0tkNYTgtKbbi0BanHSEaYLFEUch0jbUtWa0sk4n7RFLiy6mrA+73N19SDk9Ybj9DNT6qWJ9OS3L+/D968qSwXxhPi2wjDQRPnHyB0l3/kuBOOcP1wU9pRUgW3NNU9O04EBvmLK+vspLL73EtStXMc4wX4yhbDja26coilbSMWN9exPTaPK6YZ0r5PNTlMQzX2pDdjyh1g2dpEu44QEHnwVvWBmtcnp0zCLLMM6ysbZOb9DHOed9YGrDcLBCr+cTqLa3t8EKBp0uVlj0eMxisWA0GjFaScFJjo72UaHXoArpaGoP1iyBlHyRkXRSRoMh2hqqujijApZlSRQmxLFn2vhDVhsZrsKWqeO76XWlMbYiCpX3I6kdMoAoVNBJEUpw5colmrpGa83a2hpZlp2BCL2eT7oIQonBUNQFTjpGawNs4+doZWUFqw2m0WAFdVNRFQ39/pBKNOR5jtaaNPKpWsIaRBAiVEAUp2xuJpyMx3znO9/h9jt3KcoKbQwyCFHKo+nNX/o2/P6Hp396Spq19kxf3diGMIzoxV0fN17VaAR1YRBEWFn6zcIKkiihjjKcjihVQ6+jSBNHVyu0iEg6XTY2LrO9tcXNW896pkNRsLP7gLW1Ne9yniSoEkZJwt7OI8r5AhlJivkM3UvIFqc8enzK177+h/zJV77IT/7kT7O+sk6QpOSLnNe/+Tp33n1IEsb0uiOq8piqKtsORNMmWzmcbjCNZW1jlSRSCNNw753bBIGPgk8jQRhGPHnyhP39fbpJl8+8+knSNCUQsCjX6HQTtjY3MWXN3iQjjVdJ1gS93hZx1Mc5iMIO0+mcoqqYL6ZYo1DWa1gbZTna30chiMKIUCpvTKzawrV1xBcKkjBChQESTSgFnU4PjaCqM4rSR5n7efQd5DJbsLu7y/rly/TX2g4YwnchZHvQFODwXlZ/pYZzuKZuC3WBcxZHgJO0+l2Bsw7RgAwEwoCl1cd60TrWGmhJldZP+jmYs3zfGayzaKOxpgUarDc3dc4h6xxtI2rrWI9yytM7DEKo8wzROJpZxnzR8ODRAa89t0Wv0VzZ3OSbQcF8ohAy4iQrvSeEbQjiiJNpTek0r+8dkwlFWTcYEzBXDUFT8+LmJslJRtXf5+jOA8ZVTmd8QNztEnZWyLUgWYtZEQFGSA4e7yDrjFE/5vHCkFDw0luP+elf/zVu5+BcwOqK44UTwx/9wf/Mye9/geaTz2GLE7YDSVNLQtenUUNiDHnYZ5gE7G98jrR8QJAoisWMqi64srVKJXuczHdZX7lOrzvg+HSfl7Z6fGtlyIEdM5ud0o0DMMlZce5/95C89Voj//laf9/Zlp9zUTJ3fiu8h+3iv/rR3n/vG95oQMoAqRxC1jjnZaUYz27z+vJWN68U5iz/VXt6vmpFPELghPasTel8lLIEn6jkGU/YGhX6gkaFAfOyS6+zxa0bL2HnKaaUvPv2A4LEEQiNqTa8wZ+I6IYhhyrk/uNdXv70T3Ht1kv88qc+xaLKeOHjn+Ty5cu8+MpHvUm406TdBJDUVY1wji//0Rf5rd/8XX71V/8ezz33AtPZCbPXbxO3+1hRZiAjYuHYGEAaF3zrD3+HB51twjglHqzw2Z/5N7l89RpVccrJyRFf/ZMvUyzGnB4fsTsTPN97lncmUEQJl0arRKphqBY43SCiHj1laazCGd+MwRrqEGQQEjhI8PT+KIxQcUiQhIgwoAlDRBCSygiUwwqHET6h6qkkkkARtAB301h/+HXyAjXbEQiLwVA7SRQEpGFEknTo9jax1Sl10sU0NciaD9VWzIFzFuEsFosRhivXNzB1RRBVWBsQxpJRMuCZG59gczRiO7nFrDmkyHJkGDMIB9gmwzUVTXZAOdvBzGfIxnl/ImV5eP8Rb5T3uLu3h9YWoUJevHmdv/uLL1DVNffffcD+NOfu7ik7xxmTWcYrt67yd372pznan/I//pPf5ac+coPPvPoJfuXf/Tv883/5JW6/9TqPTkuiWcbmSo+j2ZheFCKsppllqCZCpwX7D97lSnML25lRV45r3UtkgcMQkKkYK7T3bxAK584Pad6TsTXJdhLpPDsaDNJAoFJiRqyV20TlFF1ZkqnCqYDJ7m1Oj8ZYqxlnGSsrQ/aPd5HhKp/9+DMcn+xRVxWrgw12DnZ46SMvEvb7PDrcY17CWm/EzsEueVkzzgROhEhl2DuF7X7GYDBicxTSG46YzDLy2lAUGbNFRqoEkYJOqsBZLl29QpKkWAOr0RajWJGz4GH2mJ37D7j2wjM0XcsltcWD/D7N0TpqZAk65Ycqp/LNlqcPSx8EaHpV9LnPFHDG9HDOnkmgApESCrBG40zDk0cPOTrcwxjDZD5jUVbMK+97o7VmpeMVBftHh56xrRtvfdP6mEgVeOaxEChjcBZefv5ZfuYzn+Lq1gZb3fQsTtwJjRXOM0raJhcX2HtGOwwW1wDK+yQqIVuDcddKeS6EJ3B+zUu5mLXf3771NNjcso6FbI2N23eXskLnCOWSce1wSzZhHCGrAKqnwbMl01CbFtSWEmMs2tTvm0ulLoJAft08LCoyJ4kJ0DLwpVYQ4PDGujJOPtBk+F+HcX4Pe6+6826NBHHOArlz5w6z2YwwSpidHLXBMDXCLgNWGrI8w2jngRqBV4kISZxE9OKEosqpy7y9Xz37tNtJ6YYxTjQoCc5ojFTs7u4xzTKMq5FB4plBFsqi8IlqIqDX6ZFGPmlsY8X71IwGw9aSJGBRFQxHfcLjGNPU6Loiz73MLokDtja77O0fUxYFla4wjSMQgl7aoygL39BDYoRlbXOTja0rXNu+ShynvPXWW+w8OeQ//tV/35soz99lY2sTh0JKRZJEbG5sEby7y+7uLhvPfwqn5FN+lxfn4H0MHbF8Cn604/sDccQ51esMoXatnl75zkLTNHR7HaSUPPfcc/zC3/x5Ll++xNraGkVVUFY5ompwTcUinzM+OaWua0ajEf3eEKVCSm1QUUhVbTM92KfOC7KspHJQN5rICW7ceAZtG8Z3pjgzB2A4GoEQFLrGCI+OxWHI2toqzp1wfHTqOwxNQ7/XQzeWxjjv5wJkRc5wZQiAsQ29XoerV69TFAXzxRTaAkkI7xfT6fVIY5/U4YylaSq0PnevVoFn2RjbYLRDBefMHIRlNvX0Mutq8jynrkuqyrRad4c23vm7aRqSJKHSDaNun8P9fRZ5ztraGnmen7F8jBaMJzOcNQwGA4b9PraCqqk5Pj3BaoNQkmy+QAJra2sIIVrmhkejsacM1lYYrIxI0y6Pdh5ydHTE2sYNqqrh9jvv8GhnF4drC+ul98KHf7B2WKzWWARKKN/9FYKgkzBaX2M0WmU2Pm6j7iVBEKG0ILc1IIkiRRClRBqkrIjCAKMU3ajL1uXrDEZrXLv+LI3xUYWzyZQwjNvEAC8xWiwWKCmpdMVwZYRQcHRyyGR6QlUv6PVj0lSQxKvcf3iPX//v/gCcYVFomrJhc/MyV7avsJdlHO4f0emGrI4GbG1tIVQbQ6krtDD0B13CQKHLAiXANBX5YsbG2gobGxuUZYnVFYNul046JHAhptI8c+NZUJairkBbtAhwGowWBGFKlCRki4KsKLm6tcra5oC6bOikQ+q6ZjGbEIcR1kCWzel0vJleXpVIqRAqxFBiJJS5BxotFiusNzDEb6xV7bDa4KwljAS9fkJdWera8da7j3jrnbe59ZEXMdazg1zrrg/erNfKvzxa/aMdDmfM2bNvpUSolnUXCqy0KClwrZGfO9OvCKxVTxWLzi3NLi3OirOu0nulUxflVMvXWvkOUy90zA8fE+ZTbGFBQ55lrGyu8vbjYw4yw9feeMD2MKF+csqzL73Cm/cecDweUxpJph2Zhu883GE6SHl2dUSYDDFZjpARpjY0umZ4aZ3pYk6QhmR5ye6Dxxy7Izq2R5gLOusfFjMpAAAgAElEQVRdwijGGMN+fkQUSJqq4OHRgr3bOdu3bjA46rL5P/wGkd3gan+Vf/zF3+Ovv/Yi38Hx2//PN3n852/xy4MRK2sKoyqaforeTCiyBd2VPjLoY9MRK88Oye8eEBLSS7u4IiM/OiAI5zSBwvUuo4arJBGIxnDl+hWOpxlKJtSVJgiflrc5vjubZlkIX2RXvvf7P04mzllR4c7jjpfFr/8dMN48XCqJE95qPZD23Chz+TORbypIJXBSUVnlGWd4u8TACZwyWCuYZBM2Vi/T1Ja33vpT7Gybj97q8bGXXyNOE2aTOUeHh8ynUxLlk5qcCrhy41l+4W/+EusbW8SdlLiXtKmNwVP3vZ8feSYp+ta3/5zJ9JD/4zf+d05OD1hZGZCmKWVZ0u12kVLSlAWLumS1u8mwP2J9Y4tpcYKVAa+9+lH+6Av/iMUiZ5IPWFlZoZemTKYT4s4V/u7Pf5qsatjY2OLo6IC6LOmrjP5mhygMEUFAU2c46T+zMAxxRqOUwoYKi4U2aSpOEoK4QxCnZzWHB9vk+349LbH8i++hp9g6ou3OuxCtQ+I4JY5TdNKnDBIwMcY0HzqmuFzzhLAYA72VhMO9E3rrMVaMudl9jevdlwkN9O0a2s5ZDTcx1SlFlVPUMVJbgsZip4fIacF0NuXR3Td59HCfurE8WVRMFjEHU8v6asRWt8fqIOLG9hZRFPDc9Zs+hUY6vv3OfV6/c4//5Z//KTc2+/z853+CX/nFz/PNb9/nH/6zL/DyzW/wc5/7PD/5sWf4vT/6Gl/603fpJRXHY6i6MVv9iEeP77G6MiAdGqqhpDnaYewUIo4ZbnwSohm7ViBFjHZeEifQ7aMowJ5L4UxrRy+EBRfiE600Tlas2CGjxRrVZIwzx8zmNWmg2T04ZP/JAUVd0e12KSqHtCHZ6T5WQNxdpdNNKE3OMzeeZXf/gKLMuLyyzul0wruP3uX+qUVrQRwoHpw0zEpLGCnEQ8voZIfPfeIFbly6AfoBl9YTymbEw8e7pJ2YThwTKUm3M8A2lm5/k7DXI5c1pZ5RYUj7PZphwF7zgHfL+4weP8P1lc/xqP8WGEFdLfjB3CB+8HHusXKBBdUyaEXLLAR/GFsyM5eyLGFqrDEI09CUM+7euc2jh/c5GZ9yMpmwqGqM8QBOEIWkXS/Bz8sC43z9bJfAkpBYrVtjYsDCsKN46dazXN1aY6Of0ovbZEspsEtZhmjlec6BNljtzWIl/vAthEDXDQ7t6xIZeODQLa3GOTO/dTi/P+DZMtJH5/nQD/dDNETXbROkTX1clkM+m8SDmrZl9y9VAdCytYOAptY469pwmvcP51xb84NSEq019/YnnJbQ6abkWhCKkDBIESIEHFLoD2TF/Os4lmmmSviWp3CgQsndu7dBGIIwZDobMxyNWFtbI01TJpMFTdXghMAKR9V4BYqKwjZu3lLoksbVxGHowSIrkVgGaeyZYxZWBj52vNKOxbwkDEICFxBEMUZruv0U2xoiS+tIhaUbRoz6PYbDPmkS0++mpGnqQclQ4pyhyBaYJPEpoi4km8yYzRas9YcoFNm794g1qCRAG0dH/H/cvVmQptl55/U7y7t9e+5ZWUtXV7VarW6pW5Zk2ZYlj2bsEYxnMEHYQ4DBQQzBBURwQ3ABXJmACGK44YaBibnARAABMUzAAGGMJ8ZjeSJsj8eSbandavVae1bumd/27mfh4rxfVlWrbUteWoJT0ZHVWZVZX37v+z7nOf/nv6SYxpKkMda1jPoDpNPUlUH3Mr79znt885t/QJb0ODudkmR9dneuYhrLYj7nopgCmit72/T7ffb393nNGSIZh6Gp/Cjgme9ufc9MnEA6725212lDvUOi8Fi8FORVzmg04tbHbvKJl19kbW2Ma2qM9dTLcPD03gbPtygOoJAWWNeEntNbMJZICByCvG5orEVFMa2taZoGqz2DtM9o0KdqK4xtQ6FRkliGmyQvSzYg/H1ryXoJUgbQwliL6IyGoywmThN86VFC0TYNuB5p3EeiMKYli/rUpqboosqQQWoTx5q6LhGuMzRtgzQmmNG2CKXRUoE0IU4vDUaFTVXjnaFtgqdNEsXY1oTNXng0UXCe7wpL44PnjXFtp8vt3NklbGyucXR4wmw6RSrFtatXSXRCVVZoZCctisBb6sqBBh1FyAiiSCMjWHQSmqZpqM8sDk9/OGI4HOPdkqLNKdqc1rXUbTBwRii0DEkkf1RB/QtdToR/33uEk+A9zgs0mqiXkQ4yTk8NxhpAgUtwSKwJrzmOA3PE+Ig4HlFj0EIyXt9mY2eXwWBAQ01VVuTzBWVZU9eWOElwXiFkhFCKWTEnkY4k1ogkwviWologlSHPLxgMBuhIs7O+yfR8xny+5OzwEaZquf3cbW6/cIt8sWT/0UOElGS9mKynqasl1ji09PjOl2bYG2CM4eT8jNliEQ7yItAD67YlSjJ2Buv0BiO8ljgkIgKkQrSS2WJKLIK/TJE36FgwGKcUtqHBcdEs2Rlsk8ggG2uaBoOnF2mMqYjiAIxKqciiHkpmeKlwhAm0tU0A1kyFaxpsN/lxTlLbGmc9sRAIJYhGCUJELEuFvK8QRCiZolwAbMKTEmjmThD0rkIgf1Cq52p10+YVcybIS4KEBSwC/dSf+6dAHC5p9CsPFqCjP3eNliNI9Dqg2DuHtyECWwDeWYQLNNUGQaJBtzk+n+GrJbPzOevjIf2+Z//RMbVpqY3gtKmZz6dEUYKfu4467ymtoWwdRoRJhI1T3nt8yNruDdppHuifOkJ7S9XUFPkChGAxDb4KYqBwPqGuoLlY0BuswIOE8WhCpAsO3plTqE2+8dYZb7/1eyDgy3/5bxCrlOODh/yr//q/xf/wv/wS37i/TxwlvPHWXV75xBZR0hL1EnoSGuFw3iCjCBH3UCqlySa0whFJF+JQqwphLTrNmM/OydIUpQTNsuLa9T0eHZxw9/gM2U2V4Enko5d/POV+RTdfHQKeAAzimev85J74CJeAlRvlByVhqwmlRATfDRFMdFFBdqo7MOKZQ422AcSR4KXo5IEWtQLuvcUQ6PBZ1se2jmKZU+Up6/2IzY0xt2+8xqPDuxw82idNU3a2N0il5A+/+fvcf/CYKBmwtr5JbzgiTdPuuedS1pskyWVj/jRgduXKDm+88QavvvYJTs7ukRfzZwDPLMsQTUMUaeqy4fT0lPF4zHjYpygK8tk5f+lHPsdstuC/+/tfZXp+xK1btxBKMllf4/6dd8h6I2IpqPKC3d1dZBUMMNu2RTtHFsc09lkwZvU6V++lUhqtI7SO0Tp+BrT9IHDzXcudZGB2eBGMpJGEmODu35VCoZQm0j2USrBSYc1HfVDxOPFEJuato8ZRNyVb2TrX01us916iaaYYP6Ey+0zEkMZNmfSvEPtdtFKYcgF2TqkTinqBKXLqsmBZ5JwvWnIi9k9POV7AO8ctsZzz1v4JWZxw49o2a9tXyHPPYjrjh1+6yQ/d3OY3Xr/L1958n//pl/8J/8W//3MUz1WsTQvuPn7Aw8fHDIcxr+ytI36o5fV797FInLWUVYMWFb7pBcPPaECpK2qrEFVLXyzp2x6x71PqWWBbSoGVgdGhxBPjXOeekiI6gRUusHVEYLRrp6Eomc7OWZNz5hdzGuVZLpcUpqKsW7TusX94wHg0oLI1G6N1ZKzxrmU8XmO5XOK8YX2yxjv371AYy8nM0Is0SV/zaOponCBNIowwHC0bjJT8b7/+Ot++c8hXvvQ5ThYXbI4yrl3Z5vDknMIWZGtDtI4ZjEZID5XJccM+WMduNuZOvk9v0Gezv8OV3SVJM0BPDLqRlC4wjr7f68NqvAqU366MrkCcJ1P1DsYJDE3bgLPUec7F+SlnZ2cs85LGeVovEcKhIt2lwrYsi+KyNlkfAHO3AttXjBgXYrL7WY/1yYgk0kRKksQKozoZhgzJU9avUjy7gbp1OO+xTYsXoUbVVRv8RXp9ou4ArXUMT9WdS0nOin3zIZLgpz9+ty3Y0z0Rqw9P9UBPS6b8U96BdDLg1cBqxbZ/um5eytM/sFZ7+EpK7JzjbLqkqBqMA+slESu27erc+v8PAOfJCul2gdYd3q+TkxO01DS2RfoQDJREMd576rqmamzoOwXBpF96lNY402KNI2/qICWVqgtUCX1nGsX0khgvJFma0DYW07GrIhl8o5SQKKEYRSnLukE4SxrFSOGDz+sqkVlp0jQlTVMa2/nrduEpEkGkM/ppiBmfLpZ445mM1uhnA8rWEuEobUskQHuFpBscDEY4AQdHx6go440//CaHR0t+/PMf49tvvcPm+ogkSWhaS5IkTI+XxJEmihKGg/EzNnLh3n32zvt+yua/t4hxusH4qsB5AcKjV94kPpiV7e7u8vkvfJ6/+fN/k34WIfC4tqUuKzAGS0vd1CA1GzvbwahKRSwuzinzGUdHJxTFkuWyYH19M5iB9XtMRmPS6Zz9/X2msxlxvM4LH7/FYpGzLAq2trbpT0bkec7a2hqlqUEG+dO1a1eZz+ecn59TlMsg+zIWZMbG2oSmqZhZh/eCsqgYDgObpa5rer0UrTVryQZ5viAvFkRRxPT8HCEE4/GYOElYFkX3ToWIbq01WkvqOkSeK6VIkojZbMbRUWgWV671/X6fJElIioqiWLJYLFhbW7s0O87znDzPaduWJI3wIkzgB4MBAtUlVhme27sOXnFwdMrFxRk7W5tEUafRTzKyfsLV3h4Ix8nREbPFPJgXY8h6GUk/IdYJ3nsOD46JdUSvn/L2g/ssliVedHF0TuA7jbcU4Dq96ke2uoOK6mQPxoYkGe8kXjqiOCUbDSidQTqHkjHONkgviKMh1pQUxRyl+sRJihWWeKToZSM2b1xlViw5enCH4+NTXOPp94dsrG2TxEF2YbyjaQusMOTNgryoMBZqWpZ1joodSSaZz6esjYaYsiFNIl556ePMZjnXdq8zWpvw6U99miTSlOMh+stfZDhKw+HZtdRlMMDbf7CPtYKre9ep65bZIufunftUrmJtbcx8saA/GDAajZCiRqkEY6FsG8bjCc60HB/NAkIvBlRl0J22dUGxLElHIzZ2tmlMy2y6YNjUDNMRzpng1xNHRImi8S2721so3UMIiY4GeCJa66mKIP9yjUHR4FyLMxU6GaFkRtlYqroA16AVCGuRNGS9lKQ/5rXXXuOFmx+nl41B6rBJPHUwdh0tVunoo7zLvsvlETj8CojxHpzBWxFOvtAlkNgwifX+MvL5ac33arkuVSAwcVy3Vwa2TwBx7OUE02Nx3nSNoUSXx9Qn99Gn++QXJ8zmDWfH77Ozs8Xa2hrfunuKM4p4PObopGR2OsMnLbZt8UIxzSvKuiIdDCnKloPWsD0Yc3gxw7pwTwmvaVxLnsBk2Of85JR4cwvbWLLxBseLCusdp/ceYYyhn6SYouJ0uuCcHu9ewIOTOVdu/jC/fu+Qf/nWOo++9Y94/dTym7/2dzHDNXbHG/y9X/0/+e//8/+IN7/2OpuFY1dp9OkUnSxwa5vUdY4eC6RWiGREduOHsNUSMbtPMirw5wvapsY5Q6Jm9KcxfbPk+OKCa1de4hMvXOOdN96kl/QxpnmSzsdl7/7M+iBA4/4Its2KGfnHNZp/kUvwBKwJNVF0k+XucCLCIUSpMPUVSnZ+BKCFvnzmLk26I4NQgfLshAvpIkJdsskIdz+2tWTRhPlUkDDgK1/+Mu/9fslv/frrLM5Ktreu8/LLL7E4P6Esco6OjvjWW+/zO9/4Fv/Z3/6vECpmOpuzubmOjgNDbWWs3zTNM8+I7ZhvP/VTP8VP//RP83f+zt/hxRdfDHLSxzOWyzl5npP1EvY21hFCkBcVbVXzxtd/lyu3Ps7WxibvvnkP4e/w8ssv82//G3+Zt779Dt988+t8+nM/Rn+QMEoH/OE3v8Gj+5rJxja//O03+etf+hRxPMKb9lIqIZXGOfHMe4cOQLQUEZFOSLIBSTpAJQk6yVDqj2Le8Mz/Cz7cEsHTRXdLjZDdVTCrSGuJ1imRzvDJmEj3MCLF++Ij3qdFMGpWwd/MeYO0ih9+5a9yVL7H28tvc9tW7KW3QVW0TYGPtrBS8nD5HtLDIN6ibQ1QwUjRRhLRy5BSsXvlGumw5I33jhikCeNxzNVRzKCX8tzuBu/e2eetu/f4zCeeY3MyZpglNE1F1Ri++OmP86UfegnnFb/0y/+UG8OIH3/lJhfVHv/7b/wWeMlf+uQ22s95cXvM8aJk0OtRWyjLhrW+Ja+npChOyznWwfMvPE+6tDyKcjaHtzlWd3B2EWSItu283whMHCmIZIwlDJ88DRES7zX4mrHY47C8y7i9Dd4wn8+pqoK6KZgvchbzCqkj7h08oDaK5mLKaNBjWlpSW9FPNVVRUuQLZos5h6cnKCnZHEqSpMcb9xcURtKLPDfG4ISj9QqU5mja8DNfeI3fffMd/tu//2s8t9Nj3NOYes72+hjTNvSzCHxL8HRU9JI+iAHTZIRTMdvZNZp+xLXBFdpPWL7x5hv44gIb5cg6QsohgvyjvBu/Y8VxfAn6QgdQPGWSu3pWno4Hv/TEqXOUa2mqkrfe+CZHjw84ODhguiypjaAWGuoFvV4PHUecT6eX4SpV0+J9GKYqwLU+JH0KRds2XNkc89orn+DW8zdIIoHSEGvwSYxXAq8kXoLrhkM4j288tgrD4sXZKY0xzOdLZot5N2RVRGlGHMeMRiP6WxudHcMApVSIpvcr02eBfUqqDas69L1Vjw8FcTo/6+BRHIx3V0k+xnka0/mgRtElIGOMCYMZazHG4i2A/nAQTj1RBaxq6PHxMUdHJ2wPe6j+iEgodAfErX62H7TZ4J9qeYn0geEtJdABhVWx4N133yVLNCf3D4MnjJS41uCMxziHxaCiBOEkjWk7uZWjKJbYzl81FprFsgQcifWMRgN219eR1tOahl7aY14a2qZlmA5wwhFLRVnneOfYXh/T12FIOUx7eBlCdNI44sruDqPRgJ2tLYSKKIqCxWKGkFA1Faa1CKFIk4i14QgzKTsfV81kkGFMQ15WyMjz4s0X2T865v79+ygEmU4Zb2zRGM/j41PuPDrEe8iN5/6jh4wGH2NelEynUz792qewXnB6fEq+LLhx/Sqt7iM9gfzx1BT5+wnerNaf2hMHusmc72LxTIgCi9KIl19+mddee42NjQ2mpyd42+KqKhjs2iAXqpsS7z39fh/vPfk8Z7HIaauavG6wxlFVFcvlkjRNSbJeKIY6DvKfpqEsS567fpV+f8jD/X2ccwwGA6RWSC1JiLHWXPrgBCAl/MhpmlJXT1xc4kh3DVmDWkmxkoS2rS+LfJBCxfiyxAnHxWxGrDWT8TpCBArfqsh7H5KdVsbHK2S4bdvLiWIcx2gdGA/GGJIkewZ9bts2GMh2JsrOOZbLJdFo7XLzWQE4WiekcUYaxxzuH3I2nyOE5+LiLExXtSKOY/r9AVrHtE1FVdVUVQXQmTfHgGQyXkdKSVEUVHVBa1wAvJxAEFg91nQJLIRm8mn39I96OQLq2423usNWMLDWWmNtkOMYqRCum+oKR91InAmpPlorWudpfMvp6SnHs3PKvCCKYuJ+zPpok+FgTNLFzS+LJbX35HkeNjcpMM5gGov0wcg3Snr0+0N0nGFcRZ3XJL0+a5spWzu7TCYToiTE2VvvuH79OZQ2CGmpygbbGpQyWOvxNrzu+XQeiqpr0SIkmTwdv2itpaoWtMYxYEKaVugopW0b2rplfX0dE2maoiRKNK5jdNFNh7TUATVXIS0A60jTGIkk0RGD0RhkHPY+oUM0s3O0VY1pa6Qz4AOl13tBlChkHFHZhtYYIum7K2bJp3OapkQlEcP+gH6v1+mjwa8mYFICDuFBSv0Du9F66540QC4YHSulQqcibPhzaS+lBStXhJVc7Dt9VVYx10/ei6dlVZdTLOsuGTq9WLO4f4A7PyKeXjA7nWKImKytA1DnOeP+EFOWSAmtk+Rt8KnAhYmVwyMjjXdPonC90hTlkiRNWDYttA7jDcgY5z35fMG1Wy8Gk+1iwdW955jNZrRTiNI+dW25uTkmyzIeHzb01jOuxBKfSrCSNw/O+Wuf+gJZ8pj14QZvneSMb9/i4g8LZtOCQW8Lb0ErTV4vSLvpo5IysJBMQ5pp5GgXG8/x1Rlxb0wxUwFw0IKDO+9y/3d+m72tMdHVa0DDxsYQVJDRZpF8xkxxZev4J7EiPgjsAM98n+/LEivXjRXo0VH0u7ViET37Ne6ydj5jiCxlFyfeJSQRmi5Ex3bp0uNWJpOCiHF/DXzC7/7W27z/9jfQts+n/Q9RVOccPHb0IoE3LY8fPuLO/Qcs84rdq9f451/7XXZ3d3nh9q3LPuGDUioIjf+q1g0GE4piyS/8wi/wj//xP+a9d+91f+cJs6qfxDgvOJifBBnxKOP48IjPf+6H+cxnf4ivfe2f8d79t3nl5Zf55Isv8vDBIVvrm1y9/jyba+vcffcd7t69w/PP3+b9uw+6gclakF10b98HX6fWGtNKUKFurQyHtY7CwU1rpFIo9ey98r3eNx80QYYn8pAQLqGRQneT/s678PtwWwoBTgg0fdYGW9w5+z2MKOlnPTwJ70y/Tr83Zv/RIS/KKZHP+NjkBWTtcbYhrw/JZIqUKfX2BhJPOpnw6OQhtfG8fGsL7wXSx8Fk2lmOT2aoQcYkmeCtJV9cUJea0WhC3Es5n08RKsFawc996VP8w9/8Nl99432ubk6oqjFFbXmwf0rWS0lTR1RUVI0nTSStbajblsa02LykKg3jSYZrHWZxSrK9wTDZ4NzuI9QSRRzq7MoLTQQ/tOAn2Xa13iNEjHcVA7nBWG4j4orYO7zvon1FSGVrW0uLoykWCCLKtmKQKIqqxbcFNz7+MdqmYjmb8fj0mEE2oB8pellC2dR87b0SoSRSeGqnMdLgrOTKJOHmtQ3ms4I/+PYDbl3b5B/93mMQM65ujPBWcj0bM9kMhrGC4G2BdSgnSdJNNrI+XsXkCrTpoVTEsn/Ow7cX3PzCLkVrsK4BX/MhlegjXfapvmm1t36YM8BK0vM0iFMv5yTAcjENcvv5NLD8vaNuG1ovyLS+ZIOs+nljTMe2eoo14kF2hvLCBxbOarCrtCTS8vLvW0FIDOq8AqWQ3cHSYV1AR7w1COsQ3pKqCCcUVdswvzjHe8/s/ILo4pwsy9ja2iJJQijKCjhRSj1BWroi9ywT50/fiX0Yw2d1DYwxlwzwp4G18B7JDgTrJENCfCi4/bTJcXivPXVryRcz6iJH+iHKO5QPbATv3SWY8//tJQEbotqFxLrwHmsdziZKCGrX0vjgtemcwfvOANsLrHdEWmGtR7qQQumMx1mDMw06TVDC0++l1MuQyDxMMsajQRi6uvA0W2sxLjBa4iikjGkEQihM3bA+XEPHCq0USIh0TJol9Ho9er0BSdZHdefxtm0ZuGCGXFdhkONd8IXTHUvIS0E/G7EscryNiHsjrmxtM5/lJF2YkYw0aRRTliXTs/PLZ/Lg6IR4I2Oeh0j1uqgpyjpErec5+HD+NliUkBj/hH29Wt81c/YvaH1PIM5KK7lqAoV0iG4a5KxBacXt27f52Z/7V7h6dY+jgwNcE1J8ykUOwtM2DeeLC9q2RglNkvVo25rDk2NOTk+JI8Wgn0E/Y3N7l9lsRlGGGMq2bZlMRogo6JuDP41iOO5xvTNSqqqG5567zvn0grIsQ3R3LKjrEq01V65sU9dtkKYMh4GWWhSkSYYzhvOLs5Ba1LYdW8dgTIMxkvl8ThyHm6vqZFVRkjDP58gy/LmUkl6vx2g0AqAsy2Aue5nM06C1ZmdnJ/ipKEW/32c+X1J0oEpTNcRxSttaptM5/X4otL1ejzfeeAOlFIPBAG88QrYsFgv6/T7GtLx37y7FsujAIDg4OCKKItbWxhhjqOsaAO8MWsdsb4+J45gkSTg7OwuAgvGMRiPWNzdo64bZYkmWZZRFGxJwWhu0tSphJfuAP0fN7He1OjDg0gDW8vTBRQjBZG2T8XiNcl6gCZGDtumMI3VCkqxR5Rfh653HICiXS6rFguHaGntXrvLJ116ll/SQVnByfMEyn3N+ekbra6QKE+IkDf4Ny/kFTeu5dvMW1/ausLG5zubmOnGsyYaQDmriOCWKFE1jqJuao+MDer0eiY6wtuViekIUB2rhyckZRbFkMlm/9EnSWcLIDdhzV2iaKlAik4SqbDg/ewhWUdc11gcPpsO6YnNrlyiKWC5z+oMRSdRj/9EDIgFJf0DTOJrijLax9AZjtJQBQU8TxuMxqVY4JUiilMn6JpVztMbijMI0DXVdYssSZxq8bbF10QFDlrSXoBLF+cU5dZPTi2NiaWjqiun5Oc1hiYgi9HDA+fSMs4tzrniLFSAdHTASrngsRGBg/8D5Gj/FzlgdHlVgEKjW4p3HexkiJ3A4qYPsQYpuWuLxzj7V2ITNeEU5blwAh133q/UtSIMwAl91G0sE829/nWj/99lINY9nOUXjOJzNuNCSNImYly2LtkZIT6RijIsQOqNVUUh1Mp66Ae9l2FijhLxa0gBZInDNkspbStcSKU2xqJGR4+DCsKcr2isRV0/GHD14FyEk19YDAyLrxzSmRvuGv/HqNsO9m0TjNb52d86nbv91/u6v/Ab/zT/7bT6xs8Nv33mIrmKuyDOMHPHgd/aZ6gWf3dvh9N5DNp9fJ1kfUFVTRLSNsCll7dBrFj0e0mSaprpCbzDk/HifoThilLZ88T/5H3FNy785gf/wP/13OFrfZH3nGld3r3J8cILxLc4apJfEqmsUPrDcisodjlwAl83/M7Rw96SR/H7IqQQC7TWx0h3TgwB4CfEk7lIHw3IhwPqQ0qiUwmEuGTBSgxAeKSO8bwFHrBRCrgyeW7zzOAfSpGip0XGf83zBxcUjqklg70cAACAASURBVGmf7ZsKmdQcyze5kr2KiS1tnZGma8STPo/Opnz5r/1LPHj4kNOTQ1779CfRsQo/xVNMtVXTtXrGPC0Ic+l3dvt2YDhubu7y9/7uf433YQiSFyV2vEasFZ//zKvgWrbXx1gjSH3DW2/d4bM//GV6vZTf+bX/mYvZgs+8/DwP3n6dX/mH/yt711/gM5/5PK9+6jO8/vo3+IkfeZVP3hhTd8ygqM5poyikcokg+zQixIYKHSGkoZf1SKMeWvXRUUQcp6T9PiKKQQiseHKgUR6kC/fXSmqlPLSrafhT0gecD34HQuI7obvQEcZUQZauNEqPsPoU53sIN4Fq/pHu05fXTng8LTrSnMweIBOLyneJpSHr9Tm3jrx8xMbWOm89/ibX127z+lmOFTOuJi8wjnfQSHpliyhSfLRFkR0TRyeczs45OM/p9Ye89NwYKTKu7W7SFDnnZ8fMZ0f84bljURlGvVBzB8OE7eE6N27cCOk10yVffGWPuigx9ZznJ447Ry1vHzs2BzmDRLG7NsA5z8myoR9FzKsKnKE4n9FawWiQcn56SJkpbly/wbI+Yat3m6KaodUGggu036BqD/HSIqiw9BkkN8mbR2TDHYrZ+2wOXsXWj1iTu2yWgsock2K5IGaan1PVjvP5nJPzGWkccb6s0BLK1hAbiVTw3vt3eXxRcnNrg53RGou2pTCeX/v9E2qvqFp/mcoKnhiJk5aLokTohhev7rCx5Xl0cMyP3oyZN57DaU4q4e6D+0x6mi//+E+gopjKtKiqwGtJeXaPxeZVomRA5QyjccLvvft1srWcMmk4OclJMoFUDq2Sj8T483I5h13OEN2B3ZsKIaAuC3xbdUxFizcJCoGxFd6F1CJFG2KRbY3QmlhJ5OIxs8ZyMpvz/rvf4HRxxqxecHFxQtM0KCFYf+5m6Ptrg6lLGmuwxiGBKEoAGeqnN1gvsF4iEOxNRlzpxYwo6Edb6CihiYY4uSCJE5zV4ARZPMI6hxWeNjGQKgQw3tgC6xg2DXWRY62hLksO9h9hmoqDgwMevv823jmSKGY8GHL7xg2ubG4SR4pre9vEiSJC4OMkSGy8CIENAlrr0CLsLQIP1uJEi1v5yjhPJCOcDUnFznZm+kiczQMY09W4tqpxJgA4kZC0ZY2pGmqjAtDgalxTYGug9SjT4Uu6+tAD9HcOxKAE3tk/ZWv3GreNpJ86UmWw7RIrwEYa930+jP9p19PeaUIFPyQnRTAslhqtBcvlkroscK1hNOxzscjBtOBS6o5cUZc1QiqcVKhIUxc5pTVoPINBj63JiMf3H/LFH/0R3n3rbahLxv0Bz+3dIIskpm3pxTFndomvayIt2RyNWeRzBrEiSxISqblx4wbDUQieaW1LUdVMhiP64zFJr4eIIqTWDCYTvFIkaYYQgovzKSiJqQKx4uJMBumjc4z7MXU9op8ZWhyjfso4Tbi6uUlZ11R5waIsuHj7Xe49fozBIZTg9OyCnUlMkVfsPX+bqrSUZcne9Wu8+Y1TvA/DqSzp47BIGT/p8z7Q2H1wAPPHgTsf/Pqnh4F/0td+cH3vTBwpgimXX03swhsZJZrBYMBnPv0q4/E4UDOtxZoOtRKefFlQlyXOCKIoYzQaBaNT69je3iaNYrwXaBV8a7wT7Ozs0DQNs9kc6x3z5ZIojrl25Xk8lqODA2KpWNvaYmdni6ZpOD8/Z3t3B5znZHrC+fkZpjakWUyWZcSxRmtFksTUdYQS0BqD1sEjpy5y8vmCszhGa4m1HudCJHLTBO37cDgM8qck4d69ezRNQ5YFQyYhBEVRUBQF6+vr5HnOYrFgMpmwWCxCCtZ60CqHiyW5uLi4ROkvGUUdIn50dMLNmzeZTCJeeuklDg6OOD4JCUSRTrqpJR1I0yDTGFtblBY8f/s2zhtMbTg/P6euywAIpRmxjoiTwKyZTRdY44l00km3SpyDXi+laRqmFwWnpxdMp1PKqiFKJFKspn6h2f9+LtE1vhJD2CIlSdzn2tXnmWanzM/P0bHGmJamCEkASvZZH8TMyyXL+YyNqzsMRiM2r+0x2dkCJFJo6rbFtY64k6VVTc6gF7MsWzZ2tomUII0DKHhl17GxOWFzc5MkllRlQ+0qhHWk/RhrLXlRMZ1OUVKytbVFPr3AZCmnB8dUZkHWi9EqCdMIqxmvrzGcjDk6PGVnd4vBoI+1DUIMO6aRpyiqcGiZzyjLmiTLuo00yPGSJOX6c893ZnFLBsM1FosZVVMChvF4RGsN1ngWxaKL/4Y4i+mJDBKJjiKiJGa2zGlaEyipIkQSR1qSyJhylrOcnSMlJP0exhjOZlPOZ+dk2tL4Gq0spvMrEQDGUDeOs8NjHo4e8OJrrxKlWUjBEQTjdC9+ECT0f+RaRYxDV4BFkFd5OgmUV7jW4ZxGKFDe46QC/aRgX06mngIQVqDQatJ/OWXKwQqP0xVD1zB//Bh7dIfrw4zZ+QVL42lEyqKcclqHTW9RNoi4x2Qw4PHhIXGs6BGmE0pn4WDaUZGNtRSmweKZTmfs9nuIWJPZEBnZNA1xHDNem9BaQRzHrO9uc/roPiWG0WhCU4cJ83g9Yxgrnt+6imxb8uUDfPGYlwc7pOdz/uN/4a/wsKx4dPiIv/Wzf4Py7BiZSH7yX/tZ/stf/NtkZw3/tHjMrSs9PvXlL7FsNIoY187pyQuQKW3e4vuKOEoxSZ+6bNh54VUeff1Xkc2Ir3zlF/i//8EvYXclRw3sRT2ElsR9zcJVpC6kGK4mplLqJ9eSZzfdp0GbD/uPFdDw1MePckkhiLRGrTxahOiMB31gJhF+v6rdKwPM1c+52nd0Z+IvlbpkxHkMxhuc6/TzPkjHnKowzjGdHVI0JbrnGY3eJ0lvkSS7WE55b///oCc+yfXxi0T9Ae+++5Dbt1/h53/+57l69So/9sUfZzgeddPp75SqrV5jXddBHiAE/X4fay2np6fs7e2xt7fHN//ga/zar/86s9mM4XCIUJrxxjp7O1s8f2OPLNb8xq/8P8zHI3aHI776T34ZoSSffuUVPrO5zXvv3+WNb7/DuBdzdvGQg/0Byyzlp//Kp6iLM5q2umQkWddiXRSAFG9p2xprDR7bMbJEOIhgiWJJHEdEKkzckygOXmVSPakBfPdEmdXXPJ2qJv0Tj53VdVQ6QekUGUVIHfFRCweSKMY4A8LTuCqkbfmI3tDiFwn35m9hfE7COre2v8itBBKdcejvopotboxfJWrAVQXOz4njmDy/YLGYsjYcMB5tcvuGZb4s2F7boqlL3r3zPrPlOYOkz7vHSx5PS2bLiNa1fPxKxo++1KdoLb/9e99GS8dkrc9G0ock4fgCtLQM44Lx3pDffPucrb5jbyjYmKyRxQqhNVXV0OtPEEIx6HvWd15mOX/MzsY1IpOxMd7mkDk/fv0XscqzEW2TZNGl90TTFsyZcnj+Ol59jvePvsrVnZ9hNn+HG/3PsaVukTx6ncZ7lrOzUKedRA1vMKlrhoOMe4/PieMYpR0npzWb4xa85tqVPSrzmMIUXCwa3jyoOalAOEVtHbGQtCJ4CgZGnQWrMd7wz98/5M7Jgp/5/Avc2tvh4HzKw3uPKSrPJFVUfsSsrPja13+bvSs7RFHE3o2rDLa3Gax/nELMWObvcd5e4Bpg4tH5OrPp76PjIcuZZOXr/VEu01ScP3obLTrgwRkknroqELa9fJ7Oc4MQHmPbEEUtPFIRgBsl8LaTLFY5B9MFJ7M5Dx/vc+/ePRZlGJKmaUqs9WWtampzuX9HUUSaJugogCNaa5o6JFWFFLvQS2odvHTiOL5kyHgRaooQ4tKIV+sYvEEoSSQc4JAkAGEgPh4ivMc0NdevbONMiKW/f3xEsVhy785d8vkFb78xZz9NiGLF6XM3uLK3S7+fka5vBtqQ0p3/VhgUBEPmjvnjVwlX/jIK3LRtYIS0waDYtgF0P5/OqOqa6TQwlxaLBaZtiaKIjY417I27ZCmt2FLec+lN8r3iLUprjo5PefToEfbl52kbS0lJmkUgBY01f/I3+QFcHzTlxnsipamtQ0fBUL9pShaLBYvlHCWD52qSaHqDIVIILi4ugs+rFszzJSKKgylyXSPxDLMem+MhkbG88Nw19jY2uOcMV/ZucG13hxvXQtJzUS5ovGUwytD9Ia//QYW1NWvDAWkcEccxL7zwAhtbmyghSYd9yrIMZ/Nej8nGJlpGtMLhhSLLMhILUiWMvWLQH2NMCBCq65qT4zNiFSOkJ69y9q5uU9YVd/cf0iznZFKTCEHjHY8P9jkv54wma9TWoOMYoSTLvGR//4BUKTav3mBelBweHvPFz7/K7OSESCps67h2YxffGkQUI1Y+rB/Cwv5u14dJ8Fcy/O91fc8gjkTgZTd98yHCDQWRjEjSlI2NLUwbfByyrM+0zDugxl+mt0wmI3SUBG8Xy5NUBy+oy4rB1ia9fp98UeC9J9aa/jCAGtZa0jimKJdEkUYISdm2xHmI+75MDFIKGYU4sCxJqDoWjFKKfm/IaDRASo1STwx94870qywDa8eYBu8DsyGKgtGwte0lyLK6GGVZBtrXYEAcxwgR0qtWlMCnL3Jd14GdU/fCxfOesig4Pz8lSTJSHeEIm8BqIroydXTOkGV9oiiiriOMc/imIcv69HppcMHXmpOTE6z3fOLjH2eY9pgt5yzLPLBGkgyQ1G0TTNAIU4K6rgP9LY4pihBxPZvNuLg443w6I8/D1+NlaBqkeuJu303bvp/LCaBztg9MhmBOOhqv0bY1xXJJrxcMPWdVgzWAlUgdEcvs8j5YHeIWiwVt08nohMchcU1L6wKLa748Y7lcMp/PyLKwYY7WJvSSlK3NdXq9kFpmiwbhFK2Bti0RMvBoe73e5QZVVDnW1SyWU6JIgA2yvjRKiSWgFEJ0MZRS4H3wl0nirGOKhftCONFFkxuaSmCaCtIe3nuSKCWJYvLaUOYlkrBJaqmCJ0a3ZNdIhFQAg3AerzyR0B1gZNFCYqQk0hGyp1DeUXkTonVnqtOPC6QIjLXKWJq2IpEeYxVVW+ObBq/oirBEJhnOePJFjm1akqQXqK6dP0xQiNjg74HkB2k9zcJ48jmL9yGxByW7iZJCCYuQEudER2l94ofxBCR49nsLF4ABrAv6cecRRFhv0LEn339EcfCAzCyZn0+5uFhw99ESpSJmRdjwjPfMlxXZMGFoHVVTY6OU1jZBG/0U6+HycBhJEpVgTImUYfoonpKg1m1DWVccHZ+hZMTO9asc/t49RsMJ1jryPKc/CKxE3+QkvR6p8kRVg0GQJJDKBeu1RGdDdm7d5OXnb/KoPsXZiubRXRYXU6YP92mv3mKy+xxGSoq6ImKA1xWtWaLbOhhxOxvM73o9jKmgv0HWG3I+q3n8+DG7Y/jsF19DpAPc9IIsGzKZjJBa4arm8v12zj2ZEF9eG/DyOyUvHwRw/iwb+5/netqcGJ4Foy4/JwPLTXjfgfAOIWRoxIXvonZX9PmwvBMdBZ2ngCowLtSTJIkxrgrJgX4LHQ9YFgXVwrAxWiOSLX/wja8z6q1R1g0/+VP/Iq988hPd/hNhTBNM+J9alzI14bHO4mzbTcgFeb6krmuGwyHeW/I85ytf+Qp3usHK+3fvsLl7hfXxCJ2kjNc2SJTg1nM3sabh3Tff4LzIGa9NuHP3Id9+6z2GwxE/8sOf5f79+7z16JCmrHju9guY5ZxRIgNII4NPl8TjpcAK1T3v7rIRWx3+wudblPLIEOcJzoDzQbb6gROJ+yMOKN/pmfPk95eyDJ71M5JSIlWCVBFSRV1+8vd6N/3ZVmUbhLc40Xl9dewPpMQP5yznhhtXdpEi5ay6x8nikPlpwe6tPjf6H6Oxc5QL+1gURbRSo7MhW7t7iHhOWbU0F0tuXNsjTSTfenxGWzdsjsb8X1+/T1l5kliw3m85LjR3jyqOp/v81U9fpZ9otFK8d/+YciNh1B9xvliwOeohVYyvz/mxW0PeP8pBCfYvpnzs6h6zZY6KNzC2ZDC6Si+WtEJy9bW/hpJnRJufxfUjPtabsDfc6JjR4RnJlMcLhxbrXPHrXOvfwDlDTI/WS4Yb1ygWdxhnz7PM9vHmkMZ5qnpBsvExDt/9A2JlKcuaqm0Z9PoUywXGOHSUMRwOef/uI1QkEVgOlobz2qOloGltCCORArky05UeZyRCtEgp6AnFbJnTqpSynpNEgiuTIfdPKk6Xjud3FTd3rrK+3mN28Ygsy5jsfo7syi5lHw6WdznPp9RRja7ByZrr27f4+CdHHDxc0h/2sA6S7KN1IvG2pZ4eYpRCCY93JtjaNmUIChAhSqG1EuHB2hZ5KX/zWBSI4EMjPbjO8mBl7TBb5tRNi4oSoighTrNLSUhV1UG+JiVVHYJWhLRY756Raq0+Pg3MfvC5994Hv5IARWGNwYZAcYQI95ZYeeZ18dHOe5QQpFl4jiSCdNgnz3MSISjzAlPVmM60fbFYkJzHGDNCD8bhtUQCoUMya2eHGvbEVX223X4iQHhBa1scijIvWCzyYJ6bF+yfTSmKguPjY6qqoui8RKMo4mKes76+fumFtkqoCnXVo/yfvvfL85zpfI51IZVYNo4kizr27Pff2+TPYwkUELzwIMB5WIM3defD2IaBjvfgW6SPwQmkDOC/UGHfd9ZhbRuS8oSlKnLyxZwvfeFLRAJ6ScrHb99ic33CeDjqrrWhahviLEWhurO7ZNzvBVmgUozHa/SGPRARw8EQpWNq3RAlMUncx8tgpxCesyDB8iLgA0IIVGqImwBqJmmE7hJ8pQyDRC8IQFBdkegwXK9MS5YlzPOSsjUILcirFm0VwXKkYjbJUTLEmtd1y7A3ZO/aVbSOgjG8c51NQmC8usue5In8/M/S733YsPC7Xd8ziCOEQDm6J1jgsVgL2XDAZGOdvRtXiePAOLCmpGlMSGzCk8Yx2doavVGftN+jXhbMplN6vQFbWzucnV2AFNy/f5+1tTV2d3eZTqfMZjOEkJe+NkHnF6aEZ+dnJEnCdrZzKQe6cuVKiOw2hrIoGA4G7G5ssShCIVlfG7Ozs4MxDU1dBl8YH5KeDg72sdby/PPP0VY1Z/mS7e1ttNadx8oAIcK/X5YlRVFcgifAJQgwm83IsgxrLXEcs7a2hhDiUk4lrKPfG+K95+jwhP0H+2RZxgsvvEA/TYnjOJh5yRDVPpvNqKrmMo59MBhcfm7UH+C97w7zGVEUMUgSRsMJ89kFQkgGg8Glv05V1hjbMh6PsVVDXYeYyihKaFvD2dkZ8/k8TGOlIC9qlrmhrgKAJXWEtWHyuJqIaq2/L+rm8MALhHvS7K8OF0mS0B8OKKohyeCCydqAtmiYnc8pyhJ8hDItDk+S9WnrhkIVNI8fc/52TmNs975E9LIBZ9Mz2mrJcrng4uIifP9+PxgKS0msgklcfzAKTDLpQevwwGtBpHtkaYqQmiKf4Uyg5C+XS46WcybDAWuTIdYYoqhP3Tjm8wUChWlDGs/p+QngqKqCsiyJo5TBYESiI0qbs7G+RlVVPHq4j3GOtbUlu1dvopKW5bxmOFjDJJ4yL0jTjCjrMRwlHZunYLksWDQL6qImjTOSuEdeFmhhUC7CmYr+eEK/J+llI9rKU6cFdaxxTUsxPcU4R21aEpOQFwvKusWUNU0DXoN1Ja6p8K2h1+sh44R4OMB2DLyiqMiGo3CQ7ibyUimcFzhp0T9gIA6CwLh5SqftTGimpO9MwL1FyoSV/beKRJiOmWeBE+ByAwYumxfvPdY+meYJwLcVsjrh+Fu/gz8/Z6g9baR4dLLkaOHQ0lB6yUlpaJ1gWVl2hoJHJ3PaOGVWtxS1Ien1WBTtJb1ZdmkAsVBYCeiIWd3QTxJ8a6irit5wgDU1eVlycVFCr8f281cpPWyOe5yfTZlM1un1etx5/yEvfeoTjDbWKYsZa9sDqqqhHFh+4saPMn9Qc3SwJK9KDvbfJhl5NuMB/hu/z7/7pc+R/vRroBuee/UFLrxBRAmN6pNmIwqTQeUYyRBnb7wj6q3jVY9iPuTqp3+C5vCEe4//AX/rJz/NT/71H+Puoykni1PWooZb169w8uiU+/fvXw4ZrLWsRsVSPjEF/qBGf3V9Pgji4D8I5Hy0lVEAWoR4XClCqy+7LNcV2G6MCZKbjiUbSufTr9viOqBEqED5v5QMAtZ4TOvwApwFJ2Ks9TS27uK2FQLB2fJ3QBjWRy+jnSKyY+YXU+68v8833nyDf+8/+EWKYsna2hoy0hhrO5Pp6DtYTK5taOoa1za4NpgKp2nMcNgPVPxIMZmM+NVf+RaTyYQHDx6ws7PDg4Nj0sGQz77yKZIsoZ9qPvWFL/DVr/4ThLfsrI/Y3NlmtszBCx49esQ///rXmM1mjDdusvnSFR7eP2Drxec4ODxk69oWqE5c7gUSBZeSpgDcKC8IAeweaxpaA4iWRGUI4RDWBzNB4/A6gPnShUQR9SHxe09Ly55m4Kyuycq/yDoue6Ng+hih4j46HWBtjkoSPloUJ4AEKx8N5yxaCrRU1HaBEI4bu/8vdW8WI9t23vf91rCn2jV09XxO95nvfHlJigopUbQkXk2RY1gWFEGGEATwo5IgQvIYwE+Jk8BveZITJAEiGM704sAxE0eDFckUTZOSOF5d8vIOZx66u6pr3tMa8rB2VZ9zRTkkJV3SCzjo7tPd1VW1117rW//vPxzycPottuIhd8r3+Pmr/x7bVwdM/Yxp+YBa5Dh3AgryxoIvkViSKEX5CYmEolny5hffotNJ6Xa7fOXeOW88mhNercfWMC0U26llVcG00nz2zYdcHnh28g5bwz5C9vnm/THCG2argiyJmFeKolyw1ZFMq4hcaUrb4aOvXGPFEGEdw6NjGp+yfXiJ7Zf+LbrXXiTrdFGtn9R6SCHDfSaCpHttT9SJNaD51LW/jnWOVVPxJL7CtBrTyQ9YpfcZ7N3g6os/wed++x+xt7XNfHEOQN7dQjYrHq0Mhzt9Ho2XLEpLN424f7agdHBnpJBKUNYhyloKD0qivQnARCNRyuGcCp4/scc5z//0Tz7PT/7wS7xyc4fawPXL23zj9oJ3H405G5/z6nGPv/6Lv4jMHfHVW9g4IdKa7XiPjs44rees7ARpEr788Mu89rEXqMuGb717yvGrPcpl9WzH4q962Bo/f4DULTrtTPD0sg1atc0UZ4mjwKa3okGJNnhABDBWOIEmSGqNS6iqhvH5lLPTUdg3lETHCVES0+mE2mnduO1vDZACzifTkNimHMYGo+NYgfEe7xxOuE2tHvb9UGdLH1IvjYNYqVCHe4mlZUW6JgA4QmDtAgjrhpYRWghQbYqsDx6YSaqwvZyDXhdb1iynU6qypKoqFsslTW0oViVmWQRWXycceoVsmxq41p+kBa2bNopcSnCecr5ktVpx/+4D7ty5w2Kx4vz8nNlFHyhIVbIsyHeqije++RaDYTjfXN05oCgKlsslxjhwIHAh3c2HPes7XckcnsVqyf2HDzmfrxgM2+ZmEwA58e2MkP4NGa5toFhr0TLCe4PAYU1IwpNS45EUxZJBptFtbPdkMiHu7pJlCdPJJMzDNA1N3KZCGw3eYr1len7OUCo+/PIL3H/vDp/6kU/wYx//kdA88MGEutPrQlkgIo2OFZcOd9nf3uHywSH7+4fkec7l42NUktJ4T+NBd3ukKJZlSZRleKXxMsx3g6BG4lBESQeVNGgdiBllmXD56IhmWWCED0m8vkEoQd5NqeZz+nFOOczRmcZliuXolGWxIMkSrLMkqULaBLVccHZ2hhCKT37yU3zhX/4BTdPgLayqgljLC+9J7XFtEqXwgPiLeeKs9/T1Xv69MHK+u3Sqdr317cSIpNoUEd1ul729HYbDIc43lFWgFutEk2cpwnmauGRnuI2IFOWq5nw6xTqLdcFM8/DwkOVszuUrx5iq5u2332Z7e5u9vT3m8wXFKpgCL5dLrhxfI040edZhsVoynU65cnTMclUyWy7opCakYRHQXJTk5nM3qIpgsCyE4OzsLDAhVqs2iq/Empq826fTCfGR52ejQGeM05AM1bJVZrPJxpB4a2trQ8VfP3bTNOzt7RHH8SaZKrCNAm0/TVOm0+kGDNLtoSGKouAWH0VkWUhNKYpig0qfn59vAJZOp4uUhpOTU6JUIyPNspzz0iuv0O92Wa1WxHGKEJ7R6JzZckUWR5RlyXK5ZDQacXz5aPNcTk5OsNZSrEqqKjCTsm5OJ9XETYH3CueaNu3Et13bVjoiP2hPHEA4JBHegRBt52G9sguBMeHm1kmMiiTG1RB5OoOUomqwlaI0S8ARxylWVvimxmuBKAqqVclsNCXOUoy5xzfe+iZFMSfNM/Z2BvTSPjdv3KJpGV5WQFM75rMVZbWk00lJkoSqaYjTUFzWZUGxXKGcJIozpudnJArSYRfhYT6bAZB3I+rSUdcGYxyrVYmQkvH4DESYZ8YY+jkI1+P09IxOmtHvdRm5Mbu7u+ztHkMUfJ3G4zFCxnT72+g4JnKgWsPaxaMZV69fCfOOU+rKhU1fBjPGxjawarBLh9QhRjhNMqT1JErSSKhNQ5xqto8OmS6mnM9GqCxDIVjMS1aTGTqViEQjsDjj0HFM2u+jsgyZ5nQH2+zs7AWQ2LXXMVD+8ELg1Q8eCwfWhcSF/0k46Fqw7SG/javWSJx2KA8gcUri1UXHbT3WIM6FPIcL3xwXCqcKSxY7yrfv8OLBLkUe0SxWfPOd24xWlruncxJp8dpzNi8gylhWFs6nRLVn6Q21jpA64ezxmDgN1G5jLcIHPyRTVtS2oTfocr4K3jhRFCGLst3kDGkc8bU332L2+DHbN1/C4pnNR3TylKq2jMcTyqrm9tc+z7jfp7fdZXaeU9cNV688h4wly57kQ7deoChWTG7f49qPfpzZ6JT5175Kk0yI4iP2rr9Mf/cqJSmXwHLFRwAAIABJREFUtq/zhBKZbJPvXsNFGaU2SB/MGMvaotMeSdyD6ZL42g1e/6mf4Bdevsno8RlpUyKiBY09R/uE3AQ5SwACogs2TruZbmJN+c7kVM5e+CN9PzxxwpNsWRktkLPmoa8tCzwtYyTEGm1e7/o1r8F5IQQ4sfH+AYnV69jX0BEGqN05zgWpFcpQW0tTr8jkc8Rsk5RDhBVIDbbxPDk549GTxzhf8/bbD3nhhRfYu3RI1TY5Ni9j3fGSkuVqtWniBKZgTdoJ3nNKqY0p5uuvv85Xv/51PvGJT/Dbv/s7VI3l9r37/MxPfRqZqFCoHuX81N/8W/wv/8N/yyeuXEcLzUdefJ7FquTOo0f81M/+HG+8+U1WiwlFfcLl/X2iXJD7DOtqrAMhNQiJkA4dK6x1oXvoFCAxziCFxPgCY33w2LCBoembCl+UKBWh4+hCKin+fMjv/dT5bwfmSC4MutdyKqETpI6RShOMjv5SZ9p3MAxSBbGzahlLSbRF4yZkKqJ0C/Z6Bxjn+LmDf5dSNczMhNrFbKXXmC/eIZWCVGRMR7ehqinLikJUPD55xHw+xeqUK1f2+OwbDxifn6OlYaA8BkksBUp7+sC89FgXcTy0PD6X1EYwKStuqIbR9C7eKJTuUJqa8XyBlBGxcty6cY1HkyWT6Yxyfk50+LfYmb3F4NVfQsuKvVuvMXzpo+37rpEyMG82u0NAb97XZb34nhCiNakV5FHE9eFNmuUBq8M+pjKo6SmrR7d5+UMf5/zsAcvFhG5viywNrGLjGr74zoQrexnGOu48WTJaCmojGOaexsRI6SjrmkgpFo3hel+zaDxTC9aGQ0OcBCn6Vl8hveDLb73F/vDDXD0csru1y8/8+DFf+9IfMi9rXjw6oJ485uj4YyRJREOFVymVqygqxUFnl8deYKThwWdP+PlffJW3n7zBc69tM5lZ7Kr57nUxf4EhhCeSpk0iEnhvNt6eGxN371vgxiEFiDb22luDkpJU69AgrmtcIzkbj7h//z7n0wk6ikmihLzXJ9IJxrrNzyZJQpZlrKoyGMe2RvrSh3vUP5WItT4rrFn96wOdUgrrZWCNiCjcyy6YG69TK3FBBq/a/ae2lrKl9jkHSkXEUUhYtS0QHklJlMSoToboJBjrseKAVV0jlMbVBrQK/lst293S/l3vEASZrnZhXXZAUZScnpwwHk948OAB5bIgloL97SHXB1sM+v1gpCxVeF0hNpF3791hMpuiWibOWkol5TpAJzCKwtr3nW+sXgiKxvDw5JRHpyOev7yPa5aUlUV5iDL9fTF8/4uM9dx4+qP3NjQ720aSqWuyWKLSmMpYJlWQjWVJCiJitZySD4eUdcWyCt40TdPQrEoia/AYZAPXrlzhEx96DSklh0eXef7mLbrdbuuLJEBrMulJqwohBKmKefXV1yjLku7WkKNbt0jjDJXExN0uqdZ4JcB4VCOhmCOjlu3vHLqtV5LE4VWMbQzS16ioncfGsXd4idn5jKapkHHEfD7HI+hEKcY0eC0Z9PrkA5jeLdgbDNnqCu6PzxhspSRxxsnJSTBdNoZvvfM2ewe7FIs5s9mMN978OkdXjrl87TrdrSFREuNl8BwKksbgpepahmVb/G+uzb8O4Hm/981346fz/vHdGRuLi+JBCBWoTt5sXMXXqSmNqTa0o0gGeYVwnriNZ14sg7GVdyJE73qBqRuiSJH3ewEQSczGiLfT6ZDnHarqfMNsCaBJYJ4kWRqSlOoKpQVZErG3s8tkOqYs5YZm3VvlrR41ZbkMF6ppGrIsA0IxtD3YJs2D1EkKgdaBuhUnFy7zxtRY66maQIPsdPMQc+uhqQxFEaLPBoPBhmq59hiI47ilr4cbb7lcUpYle3s7m6Jf6LChNE2FQjBbLoMnqgx04uUyyL0GgxBBPlvOGcZDgNbzJ6Y2hslkElB4rVmuViwXBT5tNpMl0jHdfo80ySiLixSudYGcJAlJFiM0RCUoFcCjddwzwgUQ3guccB/4GiiIwtkk+MACa0ZEe0Nh0XFK3gsRisvlhEjFwa+jgAKDVh2sa/DesqoqrDeoKqapamwZALLaNKg0Jkki0nRIJ0/I8x55njPo95lO5uhIIqKYJORnE8WBnWQJ8zXMHSjLCi8E/cEW+IazkwovBVpIynpFpmNq6wLQo3KSOKMsa6KojZqvCtI4mGeXZbnZ6BId6Iar1QrnDNvb2+S9LpbQ0UFGOE9b2IdDW5RGRCZiuZjijEVlmqyb09QLnPOBTiwlTjqUVJimwVeBxeOMxdSWNOpgm5rGGhIVs7O3y/jsCcYHhphzjvl0ga0tMguysDBRFFJ6RBSjVYyXemMIHqmQkuZaI2DZbkhOiGBk9gM3PELap1BuT4xiHSkunQCCLwYevAvyKonEeo+UCtqUAHwbRd6O4FcQEhZwJqwd1mLxiLoh95bCrJiej8mXjrSnMbMJaSRRJDw8H9MAxWKOl4Lpqg4R9laCabDe08sTVo3ACY9QEls15DKikZY0SZDCYh2cTubcOtwj1hGn0ymN0syNYO/my/RlB8anXP/oNU7ujhidzxhs7bCcTUg7fb7xaMkL+TZRlbCd9YgThY1zzmczbjz/HI/HZ0SdhOc+8TEW5ZRmuSTtdzns9Hn8oPUCkJYoNpyf3yNRGrU3YFk3CBHR8UEaJoQMqR11FcCL569zfvqQX/qFv8n0vc/xfD/j65MxB8aSTCK2jo54nH8J1ziiKMFUjjTptIaftB5wDhcu88U12fgAyNa4+tvI4N7HsPpAx8X5MXSNbAATN1G5KkgtsBaPuADkvURFGpCh+ykF0l2s7dav4+gtHkNtGmpKpO/gZEPtJjhbBB+e+AhbSrycMZ1PSfVldtQxJ0/eYHL+hLquWc7HPL57h49+6FXuvXeXTrdP3s0Qui1McUjvUChWs+CHsioKbNMEk2VXoSTcvXOPwfYOxXyJqSvSOObdd2+TpT10PAZfsJg1dPe3yfspZ4/ukiiJzrdItvaYTCasREpnZwv76IzldMIw71HOFlSlRYiEurYM8iFSrTtxAo1Cutb3pk2yWM8PKcOeKpTEeMOqmdJNciIlsc0SKx1CpvhCYdMeQlm8tW06TvAuCIb9IeEqAASgtEA4Aa3BpyCwPKy1aGdbzyK3YSxrGnSUUqkcouSDnYcEP/cg4AChwoYtpcRbT4NBo0JX1oOO9/nW6T9jmF6mWRgG/T1W1SP20l0ynVNvb6PnBWL+EG0Uh8c3kE9Oeff+XW4/XPBH79Xs5R6kJ5YSCVQe7Moz6Aj2t1Lun5W8/VjyymW4P/FYJzgcrBh0crJexN0HE5IsppsmzApLmqbMxiNevPoy6pUhDx48IN8+pn/rQwyHHTpHPwQ4bN2gkhSEaAEc2kXhWT8tCHW0JaQ4Alh8iJpuL6hpliAtad5BH9/CKI92K6qqR1E3DOuGYj7B1ZpvPZhQGEknCz56y5UnzRPkvCZT0FgoixqpBVgJUnJrKLk7rTE2sDqcEESxZLuXUVmLaWBRVEgp+KOvfoO//fOfZL6qMMWcl158jUUxxVLS3ztCRTF3Hr3DPCk50SVX/R513fBIFlAaamF4cOc97p7ssacOeHv0BslA8t63PliTu0A2VDgcATPQgEMoHcxtfTD19160MpIAgwvvQGiwHu/CXu2dxQnJfLZgPJ0hpSZNBeggW/QCypbV4pyj2+uEBkFZ4D2bZkHTAim+DWxYM+rTlo2/Zrmv5461Dh0lGO+gseg4gTYVV8kQT+6MoaxDgplUitlsiZCS09EUFcVkaYc4S0llMDX2dUEkBGmkMWWBkhIVxVilEUohnd/INaVoQWOCoboxHuGDcW5fKaqyxBjDcrmkqWoiJdneGrIz3CZJshDikQT2TXctN6vq0EDyjmsHO/SysNaNzu0mydcFq58AGDzti/MdHjp8y9Q0zjMaTyiNIdMpq2qGtoBKPnhs+7saf7Z56X2b2Po0sO/BRzGuqnC+oWkqut0eq7rhZFVztbsLwqKXTZCmeYf1jhKBi2MWdY2qIXKaTFqEFez2U1587iZ7e3tY79i7dICJJD7RNFISqXDGd0AUZ0gUipiD/Ss8fPKEqDNAph28jrFJTGlMEH05hTAO3zTEcQqEFOEQlhDmXKIzjGuwQqATRawd0+kcY0xQPBQ1yFCrpMrga9iK+5xXC4rGkOUdvDVs51uU9RjlLV2VsqgdJRUiSVkZi1OSbz24z0eKBU01p2kq+oMhZ/OCnZ0Iuyo5VGAa014JxzrGTgoBLTtesJafC4T89sZf6wbZ0+P9zJvw/e9sRn6XTBy/WXi09midhU6eD0CJI7hh550eta2xTRU8b4QiioImrrGWySRIfKJYU7YeC0kSDHQ9ZuMxc3D5UmDDnJ+ztbNNr9fFOUscJ2ztDsCLjdZOjkaMRiOMMcznc3aGQ/b29qjrkvl8SlEUjEYjut3uxt/FOc/p6SndbpcoCkh5nmbEWVhAJ5MJWgXzxLWsRghBkmSsyopmEQ65Td1QTUu2tocMh0OSJEFrGQyBW5qaEIL5dIq1gXrmbHg/5/P5Jt5v/TeMMVhvcCawdhCK0fkZzrmN4ZlSiq2tLaSUTCYT0jTd+PmMxmdUVYW0HqfDa6yqim4/J00SqtWSw8ND9vb2GI/HjM7GPHzymGqxwvoACiilSLKUOMrIuzEPR3OqqqLxDvmURrGyJc57xF9Aq/q9DE97mGrrAOElQhlEyw4TaKQQOCx5v8f23hbj8UOaqiSKM/qDDk05hzRGeUFVFPhlCc6inaeZL2nmBVf2D/FSUNKQ9buoWDAcbrG91Q/vabdLN+9TVCW1rel2MvJ+xmwyxtiKoliSZTm1c3jjydIeUaQQ1rCYr9gZ7FNFisn5GYvFjHSwQyQvdJbOOc4nU6JIce/+PZpmyXA44ObNm2gJdWFJYk2adljMl9x98ACB48b1F9ja3gYJQsX4KGE2XxBFCiU1cRKo+04qBoMBq/mCyeiMfr9PfxBAr7IOB9ElBnyDisN1n52dhEOMjOn1dxBC0Bv26W51kVj6B7sQCQ72LjE5OUMiGG7tsbWlaZqKLO2GdBkcRAlWCKyHnb1LHB0eYVyg/AonQCm8CB3q0tdEXpPK796P/a9yhMKrfuZrIWzwtnCqBbwd3hYor3HetE73EiGjFjwJohchglZ5szF7hxDBf8x6izMWawzdOGb85D5b9YJUQZ4nTJYnXN3tYRcFjx5MGTUVSimKxRIrJFXV0M26FHXz1PNs46Slpy5r0iRGJZq6KrDSIJ1AtmlLeM+4WBArTRIHfySamkejc/6vz/weP/dLP8eNW1d5+OZ98iQNEY1SUuMY9iPK+ROy7UtUyzOiJOaNrz7gueee4/6dtylNw+HhIYvRE3qXd0mUwuiUk+U5Pvf4KGK5KkmjlNHJGCcsqlLsvHCJKNMslgVSuWcKGiklq9KCzvnYp/8GXzx7yOdObrPbeO5t92D1Da7pj3C89WFO7AOgLajji4hx/LdPInj6Wr//8/d//H6MjTeBAO8dSrUKAoLmXPoA0novQIaoT+8FUrpg1tuEPUCIQJd2eFxL7TfC4rHUJniEVdQYu6J2Bhd7oqiPqS22HjHkQ6i6z7X9l7BNxu/+88/x7v3HVE6xs3/EwdE1bGN46623+NwX/phPfurHeeXV1zY+EVorhIPVaoWWigf37lNV1ca/7vHojNOzMz73uc/zk6//dFgXteZnf/ZnuXPnDv/gv/vvuXx0hcODPX7/s5/h5RdfYuv2Ftv9EEzw6//xf8I333qTw0u7rErDb/7mb/LSSy8xmUw2kcCr1Yr79+9z62iHOFYhZandg6Mouugks25yBbDFtnMgHNjatKxiQdw0SNkmU9kGiJFNiYrUxvPq2Wv5LPvmaer1+7+/ZhRIyTPmxkJJvArJIx/orBQerxzSy9YTJzzX0kzYyQ5IdcJevk2xauh29vn86f/KcXKdUTXiuHeLob7Oq0evc17dQzaO47TP0pzjnzvizuf/H06e3OPeyZivvvOYaS05SCyD7UO2Oprl/IyH0waEY5AnaNEwr0sOe5J+Lnj3ieDmnkfg+crtiuOtimE3ZauTsqgNj+cVh72I48NjmmLO0VBz+cYxH/3Y6xy98BK11HQvv4DA4pVGRHpTO62ZUE/LKS1hXVlfXyFEwFrbw5cxFmctwgUWpzcNHo2O4ODGaxT9Po//5PfY2TukWY1JteLk9JRVI5gs5hxupYyXYJVntvAkqQLnmRfglKCXwOVhxGRheTQ1JCJIetfPuakN90clUgQvCyE8aSwx1vLP/uCzfPrjP8zD0ZRXX3qem8NXOZs9YJXMUPWEV67+NE/G3+KJ/QJfEe8gIkGn7jGz5yS64RO/8Bxz/4CJcZhCE5mEa68Z/vD/+OBkLB5BI/UGOGPt9+VDYtmaxa19+5x8C8YJTaRD0l9T1dSVwLuYsiw5HQfDaR3HpDqABGVZtn+PzXVeN/Ke3Rue7rxfAHjrezysvxcS1wBqK5SK8DrCITA+gFDeGkxVM59MMFVNNZ9TNTWPT8e8e/cBdx8+4s6jx9ReEqUZu3v7HOwMyZKUo+0BwzzjhSuXyVofUZwlSlLQUfAPkgIRRdjWEygkFPrN3BXOU9UrvA32GU1VE8WaPN/h6OiIKAkynTiOsc0MrMOVFcI6Ym9prCUVgniQkykbzk6zaiMla/sLoTnxvQytWDUW4+Htew+w/BhGglQxzlua2nxPprI/iGNtw6F0hGiBwcV0Fur8asVWt8vKOTp5ByM9lXOI1guzqiqoDD3vybKYftrnox9+hY997GPsdAcbj9ZgLaLx/oItJYRA6SCVjVXMjRtX2NoekOWd4H9tLdIGYNFZg2saJIo40jRN0z5OiDgPFzoAh8h2LfPBhNyZMCeaqkb4wMzSMkiHtZC4QUNlGryA7e0tHJ5lVTGaz2mMp9ft4MuSyhoSpci3tzBVyZMnT/id3/vn/NhrH+Hx6Rm7u/u88967XH35IxSmtYXRnfbeVc/su8/MHRFME/48COYvu7H33Z2GPLjWNwDWfgCWKFJcOjrk2rUr9Pop1gXZU1UUIbkmigIFyQV2zaC/jRSe6XwWmAPGoqWi08nDJGqBIqUUvV5vQ2lfR38vl0tm5xN2d3fp5BlVWSOEIMuC0WtZlty5cyfIrfKc7e0BOlI8eviY1WrVslnC4Xp/f5/5fE5VLRgMBuR5HhzUx2OccAy2tkKi0HJOpIIvTkCHg7mxM5bFbI5xti34Fq0Dfd561gST4HWaVV0EfWlRLnnw4MEz8iuA4XCIgs3PN9Zx584dzs/P6SQd5vMlaatbvH37NtZadnd3A/umBY1msxnD4ZArV67wpT/5Kov5nB/68EcYbA8Zj8fhPWhlXuPxmMViwXR8TpqmpFG66eAdHh5yeOkgxEm3r01KjdeKYtUg2jh1IYK7+wfadfbPeoegXNC1irDRbQ5e1iBjTZJlFE2BKWv2uylprkHXOFeRpqE7kcgBdbHi5NEJxcIE6rep6G9vc3lrgMsiwBHnMUdXrhFrzXQ8J0kzolhR1grhHY8e3KMoFnTyFB0p0jQl8pDFOXXZOvXXnkinKNtwMp0CgoO9/UAqMuHAVFUVVV20oKbEGsNgMGB3d5flbE5ZFOR5jhKa+yd3OBufE8cxvV6PtNsj7vQIHV1BaRqkgDxLsF4xmc2RLqQgCJvx8MHt9vpKrl69yXDLM5stsd6TmITTk0e4xrAz6GO0pWkCFbkxBdYrtHfEdYyOFVES09/eIelkRHHG0fENOp2Us5P3UHXC9rBHVS5ZFFPq2hBHMQfHB2TdnIWt0aslHZuHwt+ZVhKgiLxu+7k/WMN7R2PeH3fpcUJukmeCkr7BSYmwGudDl02qYGDubLTRZQtpn1ljjW2ZSG2Sn/SeevyQ+vwJnZ5AO0Hc66D1Flu+xvU0e7lmOq/J85xZuUIpTd166ngRujd1U2+01FmSUnhHkkTEMsWtClQcUnTwDi0VKtbMywZcQdpSy+u6ZlR6fvdfvMGtV57nyvVjVMu2so0hSRLGywWd7R3ujk7hZMrV40N2dvZ46QDG43FgX2F5cPsddnf3eTI6I01z8p1LCDSHgwwngzTi8eMx126+wqxaEQ2PsELh6gAwbrTK68MugqzTI8s6LGrJX/v3f51q8oT/+7/5T/kJ43BFF5Mo+r3xM8bsa/lte3FDEf3UpX26m76Wzz0L2MhnpBQf9BCAEq0sgGCG3Tafg3SKtewrxHN751pzc4mVrmVWQkSEFMEHYf07xltqF/xyKttgXEPtaqyrcdJRlZbVwhPJlMPsNXRxDaqIr33pbSbnC0oB6Ij5asp/8B/9OqcnI85OT/l/f/8PSLsDTFWjlcILv5FwCWCxWLCYzfDO8fjRI6o23eQTn/pxur0h77x9j3v3HnB4eMyNWzeQWnF2dsLx5X1uv/eY115+hf/8v/ivuf3uO/yP/+Afsv/xT2KcJ+nkHF25wvlkxAsvvMCv/dqv8Vu/9VvcunWLt99+G4BOp8Pe3h6r1Yqd7hAh2YA4m3kjAjN1fcrw3oa9SHqiSAXpGY7SLPEYIhnS64RUSBmjqx5WazBN6znxZ4Gcp0FXKWUwgbzAGkNnFtmmZj1lbq2j8C+KcFo/M5c/iCEJ74Oi3aNxeGFZmCkr23Baf5Mb+Uc5Wf0pe/EepDn7apfX9v4GC3PGO+df5FbyMudyzjhWRP0ei8WMvU9+nPcevot2Cy5vR2w3Eepynz/+5hPG0jNtPNtpTO3g0cyQxaCRmDhiNq558XoCdbhfXrrkEC6hLhombk4n6ZDqmMP9AxLhOTw+JhkcMJlOefnVQ0RvQMeDdwVOxThTo+oCqbIgLRcigPVCXYA2PhyAXbteqI0c/VkvKtuEBpxdjnF1gYg7iHJOlvYYDjPOn9xHSclkEWT4H33xKueTLqPJnNtPxjRecJg5JguH05obu4KqtJw3nvdOLN1EoiPLsg7AQaQ1jbVIIdHeo6ShEwXZnTWWXj/h7ZOK09/5Iv/hr/44E19Tmglx7wB1nPD1xV2+8Ogfcku9xF50FcfDIDWsFP3ugN/9R2/wyV8+oqkSpBCs/JjLgx7zOtrIOz+IITxBqg2beyDcUzIYtrdfW9GyP4Rq5UMB0peq9TGSAqUjVucrJpMZVW1CXWxbjzBxIWW5SKNKW4PjcvN3vH/aBP3p5yOeWVue9lsTIkLJCK+iNsnOo7VowXmBrw2uaji5c5/b9x/wuT/6Y8ZlxdbOPuNVzR+9+S5CwYc+8hG+/u4D5rMJOYYbl/b55X/7dT788vNoBMaUpJ0OcaohbtkeUoPQrUlySMLzNpjgYh22PYiHtFxJt9MjSVO0jqhMQ1U3zFZLKrvAN4bYQoxEWY+tw+96JVFOotHE8VOy3afeo+9peIeOFNI53rt9hzsPHnLt0gHdKMa7hjiWz0ja/00d63nt8EjvwXlmszmj0zM0gqZs2D0e0iyXnI/GqCRDxz2EVFhTI5HESUQHxW4/Z3dri1vH1znc3SN1EUIrkihBC43yCiE0Uj2VhojAuLD/5d0QNOS9D2thU2N9jY8u2EMORaNF8NfzHiUj4iiASc63gTk+gHq+qcE4nAnNTAjJh96GfTdWGpSmPxhQO0tjaxIlMQiyRKO1o5NplssaW1dBau4dzoTzbJzCN958ixcuHdLvZMwXC6yIUEnKMO0RRwkGgqfZ+xoo367W+6CaeN8diLMuINqPoZPn0XHEzs4Oe/s7RFFEWS2wTQBW1tF4xlX4VqqjtMa0nal1ug4aUqXIshTvHZ3WA2bNYlEImjWKLYMB6rIskC2Kt5aVSK3Z3h4yPZ9gXdBfd7sdBDIUXlq2bJyCxSIAN1prVquLlAtjDItlAEv6gy46ijAmPHcpJVVVtZHiCU6Y4HGjVfA/KcoL89G22LpIEMo2aHxZltR1vekqrr151v46WgRKYV1WLJdLFEHKtY6Dq+ua2WxGt9slyzJms9nGJ6Xb7dLtdlmVgc7d6/XQaRLSiLQmyzK8EyyaOkS9ZQnbW0NUpImVDjRfJci7HbK8g10VNHUZkpVa3qdf63DbzcX67w+KfXETve+GES74p0iJF0Gre3ZyiqkbdjoDIq3ppBHONlhX443ZdGOK5YpqZYmzlE6ckMZJu6lKVJoilKeuG/K0g1KK1XyGVw4dyWBqPRmB9GQ+2QBx3hp0Z0DlG2zdtAuWZFVXlGVNEofUr7osMbbBeUFdt35OOJrGgpfsbO2Q5zmP7z8AZ+l2ckBS18EDqtPpkHe7pJ1s44dU1gXONLjG0DQVkc42oGeaJJw9eYxzT7+XIaUGwkEgSwMF31qLF6AijXUeIQMluK7LYKhZliQtIh7owAlZNyfLcpT0iCghENxDwoGzIIxHxIKsZaEhBTKONh1tJHjRZo4JH7x6fsCGJxTf3j+lcRXr/3MIHw7CyqtgMeuDsalXsvVzEoC9eM+fos4653CmfcyNL4nHrZZ0E0USOzLdCYE3jYKiDp0JPKlQLOqmPfDJwNZDUjbNxjRx3S3emMj6YIJrnCMSGilDlzJSAutFWG8rjzENcaLxwrGqGmoR8eDeY1782KvUpgmSVBNktvu7ezRxh97QIXVMZWBVNMyrCXkeTO6b+ZzBYMBiWTA4ukSa5RSnc5Ksx2g0QghPbSzXX3yVRV1RqZy8t42P0rD21zVZHOSVAsKJtk3I0sLhdJfSS9K9A5779C9w9nv/O+luj74U6Lhp17GLw/LTp+P3M3H+/z6/+NXvHxNHIloALjwhIYMvQOg8B1BGETrNToRlPch3fHs4EDgnAmiow3pv299zzm0Ym423mLZw90pgfUjGUCrG216QZfqK0kww1Ny584THTx6hkpSrx5f5zD/9J6zGp3zrm2/xiU/+teD3tCpQ6ZptIhGEQIDRaESv1+P27dvcvHkTYwyf//wXuHnrea5fv8nsLwiFAAAgAElEQVTR1Su8+8577B9s8fLLL9Pr5fzJH3+RSD9hMhmzmK7Y2drj+Reu8wef/X1+5md+JoAoOmK4vcsbb7zBiy++uNk/r127xmQyYTQa0c+7pNcPg3Q1eioS/ClGzPpr58xTh7MA3oQupcTREGpOEZL/hAjeTS6k2eFC4f30Y387xo0QAiEDO060ptWiZRU8/VzCYyi8CFQsvzZ1/QDHxfMGcIG14tvPcUQ25UH5NtaVSJFw5Hu8sv1p7s+/ytyPGFen2GbFJCp5Pv9Rcq4ADzl7+ytcvnYL42NcfEZRK/7oT++QdATCC7ZzxZOFpbaSWBmsDf4NwhpkpDg9M8jIEUtF3cBWzwSmQqlAO+K8w2g+J04yirqGJGNvb594ZwdciZcpIBHVCi0kQgYQVPnwHnsnEGsz9JZp4VwwyBQQGJhSAqJlzAXGg4wj3PgUK2N8dYJsPM5OkZP7CNUhjXew9RP6/QFFUfDw8UMqo3h4eo5BshUrRKTJ+jWdVDFdrpg3CeNFxUE3pWgMZQ3e+rY+tYhIYmuBFIYsWvs7CAap4tG8ZisTZBFcP7zJO/dvc+nqC+wcdnEjy09c+iRnzYJGTMnFEQ8mt7ECYiP56lcekEQRdeFASYTzpEkCEXj7wTNq5ZrKsVbLtlI3uV4bAS/aezcIoENjzQuUF1jfsuTbJFDjLrwYrbU4IVhLX9ZngLXB+KosLlgl/im257cZTzNwnv4/0T6+b+eMVgq8Q3mHjKIQbKIEedYlT1LSOGEQdzi6eo1op+Bfvfku1oJViuWq5OR8Ssc3DHpdnozGLIsV3U6OiiN0rNBRRKMC0CikDNJvCFi1Dc8tMHdCvbO21YiSmCRNGQwGzJZLijL46BVFxaPpKbZu6ImIVEXkQhPrCOdkkLQJ3VpKrH3ZwvX6Cx0xoghT1QgPo/GEyWzO1cuXQjKltQFc/j7u13+ZYy1+Xs8dYwJ4eHp6ykGrFOl2u5yfjqmKiv5ga7OvR0icD7VgL++yuz1kMNwm0THSCKQK59w1aPN0QyCwtWW4f7yiqe0GGFMizHdrm/YMSRvVrWiqtWRYtmELHttUCKGQBK+nqlhi6wqFwtR1sGOx9qmm3drcP9S1/W5K2QjWuvIsigPIIyzeNgEIkm2zxVuE9KRZgnJQNyXDnT3u3r1Lp5cSyQvGbWM8CIVv9/Q1dvNtGyM/iCBOoGPrTZGitEfpmOHuDi+9+go3n3sB4x1lHXRjw0E/IGktIOOsRFrL+WTEclHQmOB3s0Gd2+KlrmvSNKWbdbj/6CHYYKgqEXS7XdI0xZqahw/vYgzkacbu7i5ZFg6n/c4wHLy1YDw+4913323/hkB62NnaQUrN48ePGY0npElCp9Ohqqo2baqmWJZUVUOadkhihbcuROSVZetVo8jzDCclvV6PWEtUrJHkKCXQUrJsGry17O3sbICWNTOprmu6vQ7OG/Z2DwIYlsQbkCgwhTKm0ymRVAgd/E4mozMqE3TaBwcHZFlGURQb/e3BwcGma3j/3gOuX7uG0uG1PpKeLElClPNyTlGs6G1v0fGOaE9uOvVNVeKFIMpSVBSBLDDOtL5EFVppRKZYG0x6H+Kkvx9r4NowULYSJCdCwkCQBviNGa5xQUtZLRa442vk3ZS97Zx6vAjGiFWNdp6qCnGHOzs9et0tZDcFDA8e3KPAkuUdhrtDIqWplkv6/T7T0ZyiXhClOhgEekmeJcQ6Io0zrIPGCubzObZpD84ET6g07XB0dIXlcspodEIWR5jGMZmfUtYC6yWdTpf5fB4WEhkxG80ZnZ5w+fJxkBVg6HRSdnZ26G1tsb9/wMGlY+JkiDUV48mIyWTM6WhMmqYMd/bZ6vfo9gbgQ6JPmuXUrY56NBqxWtaAIE47VL6g3+/iOzlls0IRIZREx5qm8eg4yIWmozOilWbY75FnXVxjiPIM7SOqekmU57jlgtlqjiDowoMHRhctJTLSpJ0Og50BUZwGEAQQPkR/qh9AFs56eG83zAwgSFACRxohLMJphLRIJ/FC4SRIJ3G+ZbB5tWHieHehpXXOIWTSFn02rKXWstNN6fYvsRq/RZzHTKsF0arBY4l7KQLFIOlybzIKm0/LrltMF0SdLkIqnG9wHhpjgz+ICoBNSKUL67AWGu9c2Ly8JMu6JEqzmJ+j84y4l3F6WmA7XT77+5/l9X/nU1x57iZf/vKX2T+4TFGVFNM53aFjmCdou2Q/HVLPT9m/fpMnT57QE5pub8iTJ0+48dyr+G4f46GylsViQa/X4/79+zivmKxKugdHJLuv4HSMsSEto5fqtrAVz6QaSeVI44iyKDAdQVEVXPrU36He22H+pc+wP51SnnUpy5Ikyd5nMP0UmCP+fFDm2zFxWvSEzS9/oCNEzQpxUcytu7oWsM6jvAcVDpohwb71ErM2pFG1P4cXCC8D0NOCOLVpcK71w7E1ta0RaOqmobA1WklKVyK3amK9DJHRxZgnZ4amWnB0eY8oTfnf/uff5KXnXuI3fuM3ePnVD1GWJePxePNeRlGEwFFXNffu3eP//Mf/mEePHvErv/Ir7O3t0TQN79x9jHWSpjH86I99CtN4nBfMV3NOTk74u3/3P+Ott9/ha19+g3/1uTd47UMv8+mf/BH+5Etf4+///f+Kv/f3/kv29g7wXnDvzn0+85nP8Prrr/Pw4cMNqHP37l3u3r3LyzcucTg8RGu3adS4NeNFr9Ma2260Cwkh62hpjyNNO6HQdSCMxbs6FISuDoVkaGu3gM4F2+vp8TQT5wIoehrkeRZUXLPIfCud9Up/wCCOa8FiD8IhhcbhUHicaACD1imeCh9FJB5u9D/Bv1z8NoWdY6TkysqQ7X+Yq90bvDP/F4xEhow9u8e7PHzznMgtkG7JcnrG4ZYkW0BtFStjOOwKysZwXmlS5ZDOUJqIyHuiXgK2ZC9PGM9WLIvWLy6KkXHwt7t+dI04itjeO2Z/uMvO5Rv4pkZpSV2dIOIMRwRJFAzrnccSkoTW77OzNhxY2kajx6K9wkuFbZmmzrVAQhmkts3yHFnMMZOHOLtAxhmN7JDuXofBZfaqgnfffpNHZ49xWLTWHF/a5+qRZDRecDJz5Dp0y1fLhL0uZHHCvZMSpQOgKFqTae9928SSXNvf4tFkjnOWH37xEl/85hNu7mTs9ksOhgPuzu9x/NqLPF484uwLX+ejP/6r1EAWS1YkLGaP+fDwdc6rx4yTGYmJ+PgvX6FZhc64qSTD/oBluQpeSe6Dm4sCj/bmqZkZ1nohA1C8XuNrgpTTiZA8F9bCkrKuqZqS5XKGtZazs9HG08a10iLvBMaaDQC7DirpdDqcjUd/BvRfT5P33brPmJU/zc4JfLZ2b5ManUb4pkEAutMhcds427CfDHjhhRc5vH6Lx/MlNkqIT8/YvbTHdLHkzffusJqWYBr+9t/5VT71Qx+hPLnDn771La4cXeb6zcBmtHhMm9AYRQqpQoPNls1mLXItgNw4y6pYgJf0twbEcczdB/f50pe/wnt371BVFdPplLGzuLrheLDL4XCHq7sHHB9eDhHRSmDa5pKU5QVY0KZTfc9bqbNEacwgyyinU27fuceNK0fkOsVbhxLRB6sk+CsYoUEDzvnwmnRgjFoTiA6L2Zyf/tSP0EkS5oSmsUUgdUSed/BKUi9WuNrQ6fa5dnyF61cuM+z2wRCSE58iJlRVFZjcdcNsuWCt4Oh0OjgLi+WM3d3dsD86T5QmRA6WRRvZLTy+bSRJrYl1gnc1VRmScqVsjY69oVgV2MagRfCcs02DEhIVx2Ed8aFZooXG2pSsE1E2FePZjDRKsHKb7ukp02JJL+0wVXOaqibWCic0eS6ZT0uuHu0jopiqqTm4dJmiNHQ6XZ6MxySDLRwavAH/NKh6MdrQ7g90fPdyqqYO3QIpW6+biF6vR7/fD3IhUwUDzg0qFtJUhAjGaUKkMAVjAwtlOByGm9e6gBjWgbWipCTNUxIdUbsaoSQREWmkQwyxqbG1DWwTYxkMBqRpMHZrrGVnZ4eqDgbDq9WKsiwRIpiGzVdLuv0e243l/v37G58aISXYQPl1BMOtoihwgtbUNm3pgh4ZiY2hs8dSNgZlPWmUonVgZDRNg2l9bCKlGZsxstXyx3G8caxPkgSPJcnSjdlxVVUbxDNNU2azBU1jUCpCGUeeBynYWo61v7+/MWlOkoSyqAIqH8dUVYX3wWF/tVrhqiakIKkAKLmqonYNnSTHYqnrCiEkWZJukM5+v8tqtaCuBc7XCKlbJk6YsVJFQPFXMEX/vLEWyXqkc2sNAZKw2AgvaaxF6SATyDs99vcOqfNlAA4jhXUlq+UMYxu0FAjbbpaRpN/vk3Uybj9+TGU9RsPKNMEsuymCREqp8FiZxDXBt0EpRdZJ2qj3QDtM0ohGOc7H05BGojUKDc6SJB2yVOGl5+TJIzppQJJn0zmolCjJWHdy4zhmMZ9TlitiFRavulwhkw5CBv+kbrcfzK2THKWSDbg3G58zHY+Y7Z6T513y/gF1HVg+h4f7nJ7cZzYNUpK6rinKJVolCF1hbROiwIHlfBHu57hF2HVEFscte63CY3DdwGZrqvZrIQILwJqQMLDuHEm90dcKpWiaaiOddHisCAZnmrU/DAjU90ul8q8ZHiddYD+sqdSyvljk23+VCLHLSkZIDDhQKsTNerfuKCicLy42BwFVFdLPDBUIQSoTtrdTisUIU02QixzqGhsV6Ebhq4K865iWkCSecmUQQjNfrIhjjW0aBJ4o1jRNjYwUdV2SpinFYknWF3RyxbSoMAaytIO3JV56VuUcrWOE1HS1pCpW7PczxpVBNxl/+qWvsPvaTR7+/h9y/vgut165wd2TO+xKiR9odnYvsRAZSafLvbffot/tcf7oHqIzJOrvMs8Uu04xGU85ORuTZjknT/4/5t40RrPsPu/7nXPuft+l3tq6qnqf7ukezkJS5JAcKZSiKJEsB1m02IlFW0YsJEqCxIEDJM6CAEGAGEiCfIhtyDIQSEriILEiKTKiD7Qth0Eii7JoUiSHnCFn6Zleq6u6lne/61ny4dy3umdIiqRMzfgMarq79qp77rnn/P/P83vGDLMBvTTDioyZGcBsztrGJrr16rOidaynKZqW0jUkWY/ZrKZvQxZaEkQS0TYgY3qxgA//GPVgl7tf/B3Wxi/jWoeLWupao1tLnPmGgGD1sH4MnxN08EvnvdEr+Cz4bqxUK17Aqjv13s9IIZ23sXQbnMb6aGHlpxRCON9VFs5bDLrvUTsPQSZQWOELUYrHXWFtNJ7uocEapPOqgqYpPEwWQDbIyLI0D3g0bSm0pZQF4ZpmozclTiy7e0/xD//hPm8fHHLl2rOUesGybgjDnNbWuDbuipteoeotTJKPfvgjoAW//L/8MkenR0zHET/3c3+e+fyIxfgBWyNJGCUsJlN0VVPONTtbe+Qv9fm/f/vv8YHnrhBE6/zJH32epj7k9z7/VW7evMnzz11n1O+RRAEHD/ZZ31jn4x9/iUcP99nd3cVpw8l4yoXtDaRSSOWw6G7jmqJbjcAgnEbQokSLlWCFReIIOm5FGCa+2y8DlPPcNpAIoxHGYBFYKX1iBivLsEJ2X0s6LwFfHeyeZGx42Xrj56PQZ0U84aQPOJAhUoTv8XwUrGgw4omF267UDtIXc5yAwGiupi/y+7PfpHUFie0jpCDvPU8URNw6/RzDbIusbggamJ9+FX5gh/xRwrmvBZi3NNNbY/JEEmhQgVcvR6Gitpam8oyXVPnmCKZiUcCDtmFzFNBWnt1TNktKCVEQIlRCnOUkkaJ/6TKqt4Fpluhy7rke6gTZv0jTtgTOKzsMj1UYdJaplcJNSIewwicW8jj5TgiB0dYXA5oK2xY000OUkog6QIcBUb5OWx4T6wk0C8IA9kabHJ4cI0xDIiX3j8cUWtDqGqECFkXL2kAxqTWzufacNrdKoHOEof8+hBU4AaO1Dd54OObcRsRbD8a8cPUcmRoz7G0wGG2i55rkxDKchYib17jz6u/Qv/5BrmzsEcoeb0RTHo5vIx3U45oPvXSDk+kDWnwIRlVaVOYLKGEQIN87JI5XR8nI3zNidQ95BYkKOkW5McRu0DWQWn8PCYdSK0VBQF0bqqpi3FgqEdIEPgUyCDxvZ3k68UER3j/nlVdKIuMQKwVGQqx8+pepfQFGRjHO1L6JIjUgCGVKWwe0RhOIBh1M0aLfNSwDZCBoWndm4atkREuMtRqhxugm4KkXrtN/8IDlcslQ5Nzd2+L4NGIyL3i1XbI5HPDxK1f4xM1nuadCvvryF4jDBVefyaiwSNnSD7e7e7ZLxwJUEtIUC6yQaGOpdEu0LAi0oxFAf5svHZzwpdce8LWDhtsTxfG0Zraw5DICp3j56JDd7ZYXCPjQIGMr7DNEsp57PEFe+2KW0RJciFACXPXEBfXWzO9ouIC2rLEILm+OGB8d83D/mHMbNxDSoctTxBMFvvd7PKn0XP372yl7/dpjfWFQSTrxJWkccW7Lsyt/6JM/SFMUTB8dE6mABkmta8IkJheSZrYEY8mTnLWhT2lOkwTjLKH0BZSmbCg7oQGdu6bWdVcTCJjP5wgZsCzmHvauFEKFJGlAGsWP0y+lP6tZ5witpdIGZyKfiGa0f9EK6wyuw3U82TBrdNs1UDzzzRiHVT6MyCmDVI7a5BjjRQ8y8PvzMOgS4FRA3WrCJEIJxaJdUlUNMkioqoY864MzVK1Pan73dflm18N1/3vyev1xFwe/SyWOII4i6OTVQvnIOg8GDlkWc/qq5yFekeeHlB1EePXxq5julfKlbQxBYFFSdAffEKsNArydaTDomDU1vSz3qTxFRZb2PRukLphPp9y9e9tDlDtOy9b2RmdFgd3dXYxxzOdTGt3yB1/6Ijeu32Q0GmHOneeVV18mEJKN7S2qxn+MP0z6g7F03cQNPc+m0TWOlunUx5oK6S9onoVo07As6jMvbBBFtG3LbDZjMBgQdUWVKIqIkoTUOSbLOTsbW6R5TlmWlE1NVRRYa0nTtLNRTXHOsrm56TeVznH48IDW+gLWpUuXcM6dWb3yXkJZ5Tw6PMJ0aqAkSbh75z7HkxPyvMf6+oijw0cURUGe56ytrSFERCB91XS41kcq6OUpV65e4HR8zGJhAIGxnqcQRTFn1pj39HTtFTg4vzFRvnKD7GCcDnxqjLZo6Vhf3+alT/wg9XyOMEum4yMe7N/i9OAQo33HPQxihFBEWcrtg3toCwfHp9S65cWX/hleuHKZ09NTvvjqV6iKBdevXwc0VbnE2JowComj1FuJVIxQYXdwfpxoI1WLVJI4DBAuIM8ihDVkWYKpNHfufI3FdMbJyQlb23skacD+3X2apmHQX2M2XaKcYTRcx9QVpdMMen1OpzVOSHb29kjTnFlRMdzYJHQZSsJ4csz05IiX/+AL1FXLiy9dYP/wlChOefrppwlCRb835Pjo0CcLtC1lUSOXC2QYsJmmjxelqCs0Ladk6QAVJOS9ASqWLCczqmKJM5o4DBmtb1GVDbfv3mE2GROGAXGgaMoSnPby8ySgaWoeHR+S9FOCQHZWTeN7ZRL/EP+nrnjjhxMOLSuE46wrYDCId/1nnVeFOWuRmLON+yqtBfyGX8l3doUULZ4rbFEiwmjJ/sEYZRu2ts5TjR9y/tIOb776iEGYoRyspTkPJlMGac50McZ03VolRZeO5S0xZ+tUEBCEIbZtujVaEocRaRRT1jVpHFMUBW2rETGkScJkOidSEm1bKhxZEvCFz36FT/1HP8RP/uxP83d/9TN87c1b7F7coTfYoGkbjk/GWFeTJiFpf4jIMobDIfMW1ja2kCri/v0D2lpjCFnUhmB0nkVd0pBxbvcpdLZFFAU0ZYECFlNvy5pMS7RpiJKEspmw1htyenwPZ0FJR+RqIiVRtsXlW1zYucrXnwn4/KunqHRGXZcMBgOU9OkfT9rMrHjiYbwS5zyhvFr9+93WGv+GP9bp941DPPG9iA7XIrruJt1B0smuKOr8Edt2EdVdJ8k5R+ex9GvXiqPTbcCMNZ29z9tDpFwlqDQ0lZ9Dy8VXEC5CRgGjywkjK1kWfRA5h+MHzJdLLly6wltvf5XGLnj48BFHp2NmiykXLlyirn3yVBjGXHvqOj/5U3+K3/703+N//h9/id/73O/yEz/9E7z62sv8lf/ur3AynbOxuU0+6POpf/lHefrKBay1fP4Lv8d4NuPC7h737t3lF3/hF/hTP/2TXLv2Uf7Tv/xJPvVv/AWevvFBfuWXl/yH/8Ff5MWX1vlb/9Mv8SM//M9y7akrPPPMDcqypqoKitMjlmVFnOQ4YbHOYmyD1gYVBlhrsK5bt2QHKgXAepuT6OJ4VYBSMcoFCKE6gKk/TFqrsTrAGI20AYh3qqrgG/k4T845azswtbFY12Btg3OtL+pIv7F9r+ejdRrZBR8YsUJA246r0UnSrSZRA46L13BhQc+lTMSMkdllrfc0hZiQpo6kcfR7H6BsT3DVHtcm56jVMfuhwJ1LaKsD5kdTFqVGOJ+e4qzm/EZM2BVVi3pBqx2zUrK9JogDyWKhGeaSRhu0cTRWYQiIeyn9YY/rH/8hstEFjG2QztEujrHxAPHwiwTDXZIgRtcVxAIV+KZGgEBEDq3tE5t66Qta3lMDzvPvnABjaphPaMd3sSf3u6TACfPZhMgOaB7dQoS+iNBbG3L6yte4f/8ecRZzMplS1HDu/AW/TpsptrVo1XD72Dfdqo4ZFipDDYQyoCwdQip0V6j9x6/e4pPP7jKZjRllAa6dECQps6JkWd3j0XiM3q5I5QY/cu3HeSN5meLem/yjtXtcqvd4evAU98r73Lav89rDO1xINnDOETYhx2VBEodUjSYUitPjU5829D6PJwuh8FgFY+zqda4rxot3rIHzYklVe2uHDCJso6nqhqJYoKSk1xsgYg+CreuasizP0gFt1wAVouvem5Y8zUAKmtIn1i6KpW8KyAAQPsTjm5zYnvy+w1DhnCQIRjirkbpHv9ejaRqqRcmFy0+zLCvevn2P2w+P6PVzruzsUk7GJEJwYXuHLE/RlSbNesigK0ICSNGplvxefxUH3ViL0Q1FVRPlfQb9IQ8PDvjrf/UXePnrr7N15SmIIh49eECUxPy5v/DzRIHk1/+3/5U3Xn6ZyZuvk7XfT3DtKsPdLQgVKozPeCrAY/bYHzXQzDnCQDHo5+zu7jCdTvna11/hwy/c6HhG7888/GYH/G9m0f1W7/uOtb9bV2QgCaRvNDhnyLKMmzdvevj0suLy3gU+9PxzzCZT6vmChw8OCAY5aRwzcY66ahFoNjY2WF9fJ8pyllVBisIIyeHylLJcnrFgi+UcbTxWxRnLdD7z1nspEWdKP98Ivri3y/poDYBIBagkAuWFCl5FbVDShwIJ62jM4/TrMIgQJqSq/dm46JAkKwC4MQZbe/VWGICxgtFgyKwsicKID3zgOW7fvcfy/j5OSJ+oG0Zs753zSuxAeA5p3ZANRvT7Q0a7qUenZJkPvQlCrJB06MA/dKzs6+8u9ryvYGPnukg8a9DC4RqLQdDrDdjdO0evl/vNYgeW9bLrxw8wY4yHtNYNxlk21zeQgUQ3LaKDHUkpCbqEhclk0iloJKs9TFEUOOdY39xAKWhpWB9tsrdznsVyiXASqeDo6KirAoY4PccKfFy4VJSqZrEsCcKYNE/Z2jxHWZZEYUJd15xOxp3CRXL+vKLfz5nPl8xmCwaDnidhB8EZsydUijCKCJXyEi2haIXl4OgYpRSLwP9se3t7xLGvRAZBQJ73AShPT2lMQ9BGaG2RyrK9vkGaJFjnKMuyi462XLx4kaZpGI/HSCnZ29ri/KWLjAZDrLU8PD7g/u071KW3B2xvb/sNRRBwOpmwLGbs7OySpgnHx8ccn54QRRF5LwXhrRpJnpFlGU3bom2NDCJ2d3e4cGGPKAo4PBjjjPNslFVk7ftwUnE87jYivS/SGds9bByhijBtg2ktTa2RLsI5wcP9h4xPDjg5PmR++oiqqJFhQr8/wjrBrK7RITgCdi7vooIQJx2b6+s8dfU6g9GQL3zx87zyylf4yEeep7YVGFjfuEiWZYQqRRhF2TSEQcZyNiPJM/r9Pm/ffoMkibl0eQ8HJFncyQJjZPyIYlnTWl/oPDp5RNXU9HoZTRXirGXQGyLRtNWiS4sNuHv3DvPGMBptEEUJaZoTZSOiuI8U0B+sEciOkxIApuT4+ICtrfPkvQHj6YwgSoljX10vpnNkoMiSFNeBrMenU6q6oJ9kBKFkVi6ZH8+wQ0+jH/Uz4nBAGgRMZ2Oa1hFFCYenhzTzCq1bEAajLY1oCdOIvNcnygK0MExmJ4SmYbc9T6RWdjP5jrllvYqRIHyPp9q3GRZDLRa+c75KIHHvLuGsijgCRYgkAMdj+46VrOLXJe88mIXCgwu1BVSCtQ3VtKSXBqgsR8QJs2LG3lOXaQ/HnucV5EhtMbWm240iA0XT1FgCVBicQY211vTTGJTyGzNjCMOYSFuk9CpE0zb0kphaQxRG1LpFIlgsS9bXt5nphnvLBV/+WsnPzE/45D/3En/jF3+VF2/cIE0DTsdTpILzaxvIKCSIQ+L+gLg3YNIakqzHdD5nMx9wcjyjqBqK1hH1h1gbsnXlGuu7eyxkn0illOUMJQVtXaIELMcLwihBOJhMTgkDxendtwjqkul0xu03bjF5eJ80lKyd3+bG9Rv01td5/oUP8fx//pf4sR/4ca7v7aCbFquk5zc88RD2QWkrFop/+WYP51X86upw/X5xcRy+y+W/L/xJAdeBRP0z2RrbeeFXEenOW32gkzr7tdTgI+39M1xjtMF01lvRFX6s87Yh5UICIXDGoIVEYNGuxQmvwhs3M0a9C8S9hNtv3+Lrr77Gf/mX/xN+69P/F7N5g3USoSSLxaxjt/l0qK9//cVfCQkAACAASURBVE3Gh4ekacqbb7zGL/3NX+av/vW/xr/ysV3evn0HtbvJ89/3MXCS8fGYry9nXH3qIjeevkoYJ1RVxfbWiONHR0zHR/yDz32Zn/jJf4kPPn+NBw8fcPhowc/9/L/LjRs32Nve4PXXX2cyPmHv0mWGayPSXp8sCVC2wtoabRvAW2Cs9bwx6zTOtQjZFc2cXt35iE6JIrAoIYjCCKXyrtDmVU/OalzbYITE2BZlO0YYj11979wUeqCjt8R0TB06YKprsbYF0eJEhZAaFViC4I+/M/iOeegcVuiO1eOVhoG/QXDCq4xwhkRl4BpmaISVNK5mN7jJhl5j5o4JdU6itjiJCor2NrvxZZL0PEfjrzKvHzGuTqjnY9bihOxcxKixjKdzDk9nVEZQTWvKRuOcYDNVaCNYLC1tAEVguLiRMZsXCCVYH/TJsgikZHx8Si/NCdoao/2esJ6d+Fj58QHGtegv/B3anacJLnwcRe0V03GCFRahHQEObd07Dmdtx1fEdTolKXCzMdWdz6LSdZriBNPMkS7CCEX96I7vEuOIkpQkSvmB7/8kp6envP3WG2yONpnMlkRJysX1Ta7uWe4djCmairW1kv3xAucCjhaGuraM0hDrNBt9b+2vkTgHwyigmJ1yfr3HcOgP/6U1lPOWF196jvVRDI9a8j3LV177XbZvfhApbiHKlNfql6mWlvPTXRa3Kj76gx/meLxPqHocFw/pJznaFdhKcTg95dz2Fs7deQ/nImewdNdVA5R63Fk/O6d0fCrrHoPKlZQYayjLmsW8oCxrdNshFlqDlQKtfbJuU9ckYYS1hiQOz/bJZVlS1w1CQGu0L6R3hfY0iNje3sIKuHt7yfHxMZPBgLY1Z4UjgMe13G5NcTxmjHT2LfAHWOciRBCRBCGJdYTxkt66wDnBtZs3MFWLblqK2QzZlFw4t8X25hBtDKEMCUSMcBK3sp2hujnrVZ1CKYQEoxuccySDAf3RJk5K3vrSK0wPD9jKU9rjhxwcz9DAxz7xIX7iBz9BFifU99/i5T+IGR8doKsli+WMOL/CYH3Df72TMXVdexRHx/D7owq3RBTjqjnOaHbPbXH7tde4desWp6enrA8zkBHufTjDfKu1+JuzkL7DzyoErdHeoYCFOPLp0FJyeHhIKIwHYFtLFucsp3fJA0UgJJG0qBCSMGBra4utzXMEYUg/SggQHB4e8ujoEXXVok1D01SMx2PKzspfliWTxZLaQl0VYC1RFLE5yAmk4uTkhM21IUkYsbY29C6VfkYoPWtHGEtd1SwWC4zxQOPVObvRhlaDblqcEixmc2aLuVcL9fKOm+vvE2tadFWT5TkGQZQrfuCHfoinT8a89jf/BnGQ0gYtMky4cf0mUsLJ4UOUN+OQ9ddY29xkZ+88+weHDDe2GWxs42RI4Nw3S3z/hmGe4E0CZw3Bs8CB79H4rsliQkqMtV3c2+N4wjiOScIIFJjWWzDa0vtIsZ4wbjvpVVPXXVRZ5AGbGIx2Z2/zfjjpbUVVhZQ+4ccZQ9U0vhPYaoqiZjyZEgXBmfWoqWtiFdNLMyrhpXdVVWEFpGne/ZlS1zXz+ZwoTQjDEGMMdVlSt22XfDElTXIWiwV5nnuLR6vPbC39fu4LTM4grENah2k1ThsvJ0RgWg3WF75WB6aVtUtbe7ZArdQ2MvBx5qCxBs9QMcYfwDoPvO0+zjnHzs4O61ubaK0Zz6YoBOWyOOu6ehlZfBY9XpRL6EjiTQc99uobX1V/8vq2WhPaFmssygmyLGdnZwelFOPT5RmcFzrmQvD+xD6/u4vymDT1+DDl7c4+/rGpLVXpIxClhKpYomuNsIJCFcgwwjgBQUgUhiRpjooi1jd9kawqS3a2znH92lVm8zmL5ZL17SHOQZyHRGGEaS3lvGFeLEnSPnVrCLUgyhTL5YJlMaM/iInDENPPibM+xjiCKCJOM5JQEgQJDw+PvQJnbRMoqIqWXprR6pLTkwVb+YgkSSjaJb1scGZNDCNf3Q6CgCjIyLKE0XDI7PTE+/Jb7RfsKDpTx+ku9W1lZdJNQ6QCglAS0hXrtJdpmlaDbkEqrPX317JLyoqzCD3RNB1kW7e+Q6AChwpDEiloyhotGuraMhwNiJKQ2cEYUbcsy+JMBbAC/NoO4od07+kB5DsdDmgx3eEXhPPA0tWhnlXnVTwJKu0STFbzVXglDvgOvuCJIoBTWGHRwq+hSkRMJqesjy6yWI4JjUU6yPs5blpgBY8L6A7CIMAYH21ruoOn7669s5vfdlwE0R3ynfEdxyiKUM4nARjTABanDTL0SgIhHLEVnCxqxkHEG1/6A579F6/hlOTB/gEffP4G8cjDwfN+SpqEZHlOEKWM1rdY1pqT8Sm6aemt1TTa0jYOq2Jap8iilEVZkzQt60lMuVgQypZWN1TFkjxLSMIQZ7XnfY0nvuNbTPnyZ7/AG7fucO/+Q4rpmEDA9rWrfO3NB/TzlB9eFFx97lk+9NEXOX7rFv00oaiWpHnfb+xZ/X6+OZ/kTGzzxEbryff7bjdg36th6EC3dB06Bw7/HPbzzjyhsHkXg896SVnnOvC2oU7FYJ2mc8J0n7MrXlmHdoBzKBl4IGinPBDOM3aE9Ew7KX3j5ulr1/nUn/4Z3rr1GotlwY0bN/0mM/SW1KZLqvrc5z7P66+/zka/j5SS9c0Nfu03fwMZxfz6//tVnnv+BRaF5c//8z+FUiG6mXN8/22MExw/OiLr5cRxzEuf+BivvPIKTVMTBJLf+I1f40d/+F/gb/3t/xNja7I84s7tN5lPDphsbvLw0UOcCojSlCRLkWFAs2yJuvQnZzWK1p+hgo4rxMo+7rpSnz9sIbwNSjzBu/G8h+DskHSm/OoiW41pEXwjw+ZJVsaTH/fkizGtt1bR4miga3i8p/YVnhD9CAsopJPevuMMTniQqcBhdE0c9ljIJcqFCBRB7Tkg98ZfZmd0nbYJGfU3uZp+GFuOmdeaIN8kkSHrH9CEoz73vnwLJTS9LKSuA0JhKa1j2UgWtcBYSVEbBglkKSh8ylhVt+xs9DHOMshzzm2NGG2c48L5i6yvrSGDAJolloRmMSaQIY1uEbZBZhs0ZYWqJrSNQgw2vTXDY6cwWmOlB6d6mDX+T2toTd019SKKB19H6Yjy5IQwHRBnaxzfuwX1jEVVMT26z87eOVgWVEbQWkdTThBCEoYRVo+RQcLxZMLJZMGi9BaFk1mJ1l1Ur9REmWIQeXWFbjW1DLGNYRVoYJXjdDnlaNYwrVseHLb8pZ//E9x8/hrl/JiqbNgc5BxNlgxJsek52rIiGmyzFUT8/X/0+3zk2csk+8egLY0+Ydgly9qqpW4EW5vnqNv5ezsZ+eZcs3ffS7hvZFL5ArajqTVad1B3o2m74lwQBGhdoNuGIAgIQ28CXx3YrLW0xnhxo3p8z65ux6qukEIQRCFKiTMb/Fl8sxUe6ircGXHND39f+b/6w6UEjAoQ1qu8kCE4jYxiWK0drUVJCCJFtr7m0yelJCWj0S2kid/f0RWR5eo5phDycWF69XMEQiDCCKEkVWOQwvHiR7+P+aLg/sFDqtkMFcC1nW2a02NsIFmLFXtb6/QDx/bmFv0s986HJyw3qyax8xU4/qhVHEm3BFnLoJ+T5xlFUXB0dEQWnyPP5ftie/5W4w9rAL1bqfOt3sc6i+pUtStswc7O9tm8UkKdMVqNsei6IVQRKvLq7DCOcErhlCTv9RBGU7ctGoeMFVKHRFIyWJdEWYXVxhdRigprHWXVUBYLoiDEtQ1ZEnXhJJIoVASBx7GIQCFyC6FDGh/mUxWlP281NbrxYObJYkFjOLsnjk5PqIuSwfqIuFye/c48rkIgjKXxtzOJ84reZ555liwfYY7GBGFMawxaN/R6vsi0t7dHaCFOE4bDEb1ej/mbb7O+uY11GtXJ4ZzzTpQ/9Bp2U2ql7Ftds9W/v1fju7NTIbAdXCoOQmQkO65LTJwkyDikLGY47Q/4xnnugrIBRVtj2y4+WRusePyDRGFCkAYcHh76NIjjY5Iworc29LFlyh84b926xWLhU6Pu372Ddpr1zREySXj46BAhBL0kJcsyJpOJJ6wLX/wIk5DBYA1tDFHWUi5KkAqcQCiJEoLWGBrt/ai9LCeMIyaTMUoposSDVieTCcPRgDSLOTo8RAhHksQUxRIsJEkPKTkrjARB4FUNvR7wWEk0OT5mPB57i1Wn6lksFqRx1qVleS4OgGl8ilfbau7cucPWxibnzp0jTGJm4wmT+cwnZCXeumW1r7YnSeY5RaKhOF0SRRFXn3oKaf01iNOE5dK/fm/PSwzbpqE3yAmCwMvZlUCogCgOeO65Zz0DyCq+8uWv0Vrn6fj4iLj3ejjjbSm+4/yNN5TowLhSrgBYIdKF5GlKLEeEYsni/kOm1ZxlsaQxAiFDRN5juLFOGCkGo3V6vQFr/XXCIKCpC4qy5Ma166AcR9MD9nbWCcOUqmw9ZLpWHD86xbWCXr7B2tomQjim01OOTx5xdPKI2fyIa9eucfHiBe/PDEN2ds6TBR9huRhz+849jsdjb9dz/iA9OZ1zOp3g2oamKFnOFVa3RFHMaHuDne1zbGxtkiY5hUhoJaQqZDgcsTFaY3zcY74o0U1NlidEkQee1XXNbDrxipwgYDQaIRzkWYZ1gqq1DPs9yHKc1RwfHyOEIE8TpIC6XHJ/vyFNU7bPjej3+7RNRVu2JFGCSkJ6sWR+Kqjrktn0EaY0yLDHtCowoaLWBbosWMxm3QbX70Y8E8KnWQkpeA85iN/xcDhaZ8/iz4VwSGG8FWXVbRHCp2ytDvXvnq5OPi4ISHum3nHOUdsIKUG7FkFNEiT01jK+8upX+L5LuwQiYjQa0QZLti9f4OhkxiuHB4gwYDRIqZzCLL1NMwgCrzhsG7T1D8M8z1FKUjYVaRSinKf+Z0mExlFVBWtJTl0WtE2FM4YwStC6YTBY4+233+b5yzfR2ZAyDvjM//GbLCrJv/Zn/nU+86t/h+PjY+JND6SvdcD59fMkScZiecrB/iHxYANhFefPbfBw/5DJeEFRNsTrKff3jxhGFTfPf4LZYs4WjkBXxP2EyXKOcobDB/fAamSYspgtuPv6G0yOH1KXE37rM19i2ToqmaLxyYSXDyzTFNLS8vlf/DTr+e/xH/9X/wX/2b/9b9I0zZmM+0mI7JNNupUa51tJZcUTcOT3o4Dja6D28b3iVnacTruPV2bZM0nwqtjYMXy6iGthfYKOxX+uVYEA5OPYZCGQTmAJkbal7QpDOEEktGcoOIE2vuAViIjRYIOL65f47G/9fxSnU6SU5Hmf2XzJn3jhQ8RZSlWVBEGIFAHL5ZJPfPz7qaZTxM4eO3/xHFt7l3jj1h2e+sJnGY9P+bc+9VPsxi2vvfZVfvetAy6MMp556irlfIFOmg44rLl25QrGtjz3whWuXr7Oa6+8xbWr17lw9TL33/aqgMHaiKzX48qVp7jx9E0uXrlKf9THmYpq3qNezJjPS0wrsNpijWe+yO6k4JwHG4dqRSCynW3Ivw2rO8aAQkgF0r+PtZ4D4KRnVCElwno7hRTqieu7ShFbFW3M2YtnmlW0uqZta7SpO3WQ9Xaq9zycyitR/Be1tK7yG2zh2UsSRxYNqeoZi0bjZAsyRgURQWOYcJckyZGk7PV3GYkdbj34DIU5YRodQ2txyRLtWrJ0yN6V6xw/OOLgYJ/pfMGVi3s0ZcG94ykH05CyNKhY0hqBtZJAOSIMlpB5YYgCQVGVTGYhOMnHXnyJwdqQsm4I64KiWCADhZYWZ1vK5YK0WmLVCaxfQbYlcZDTphlxGJ/ZDqkLTJhijEVgsbqgrSpYHtPO5xi9pD58m0AaXNtwMjuGeonRijBKqZsFlz7wUer5ksYZwsjSS3skyQBr4fDwEO1aTo9PcE4xyEOqqsDUFXFgaVpDFFrW4pjGaBwBjRX0Y0lPpiShJRWWwhjKpuHRqeBkURAFhpsXc/72r3+ajb+fsLeRc/naBWabm6T9TX77M5/m6oXnca7iufAFjur7fOwT1xiXC+61p5TCoKSjqWKiJCeI+wx1ibUa17yX89BPwVUqI2cqy2/05/j13iJEeLaXXywK6rqlqhpwCiUjlqVvBBetxcWeN1nXNf0so5/3CAUEcYRSinnpizJ0BztjOmOdlEgHEY48ikn6OYdJzGwxp24bWq0xrcXGoFTQZb/4OSQ7QcBKRWmtD5RxQmBljAwlrArqKiRQAWVn9YrjCMKuQQE461kiGoEyGhFFaOXXJp+qCU5IhPR7mVUKnjUtwhqiUHkbIYAU3Lx5k0+89ANMp1P2H97n7TfeIIwU68MBr33ud7wiaj7m2rkNdr7vBXb2dhmNRvTX+iS93lnha3U9pJRY80f1UoFoGhIpyKKAy+d3McsFt2/f5o1bb5JnEcN0zYP83+PxrfYHT/7s736/b2rZftfnVEohhfBzzDk2NjbY3d2ln/eYlTVxGLI1WkcvNGu9HqfFnKpcMEgSkqjH+saIOE1xgSMbDkFKFtMZ8w5gvGoAW6VwAt8IdpYkz9iKErRMGG6uU0zntHVJUxZQaWLVEAhf2EmjmCTrE6UWJ7xlsLWWpjbUdUPbGA4fPaSqShaLJfceHlB0iBQhPXc2TGLG+8sOGA8oST/3SsVYRmjnQ5WS8ZDB9mv8yI/+OH/2Z/8c//V/899S1hV1a9jfP2DYzzCtoa5b4iyll+UsiiUPvvxV9vf3ufHsC7RVS5AJD4EX316Ko3nn/vBJWPn3cnx3dioBYRJinEYEglAJ1tcHPPvMNZw1VEVJW7Vg/YJSVQ0BAiMltAZsS5ZGBIOBT9oJBFY7rBUslwWxCtja3GR9MGSxmDFbzhAIz2EQgo2tdYTCQ38HGUkvIh32ia0iV8p7lduWarogcgFpmKFCgWlnSFPQjOdYa4nzTbbOnQegWiwJBSSjHuPjE6bHB8RJhsozGq0p5pq2OiGOQwabPWQEJ+NT6rIiDzKUkMzGc4ywrK1lOApabYnjlCyPiaII3XqlzqKoqJoaiUBbR57n9AZ94jDxG67GkOQKEyomx0eoOkDKgKPFlKPlCXXTsD0csr3RI05CHo4fsShrrI7p99ZJk4ys15ImUDWa6aRgNvc3XZr1EUJw/PCUqirI0pStc5sMBgPfSc9ictHzKo0gIO/3SJKMk6NTqqbBTSdsbZ0ji3Je+sRHKcuSBw8OmM+WniIuJfX3dGp++6GUwgnfKZBC+i6fE151oDxsVgjfGbbOIEKFTEPWNs/TNH2ECtn9QEwyHnNv/wF17WiMJTWCq7tXcVKQDHoM1oYsq5Kj6QlREpIOUuJ+QqS8JUVXirqApmjQbYOtCvpJiuzHhHlKGKQkaYR9sCAoWsRsiVkbEYqMpL+L71Aa4qih3xshMPSSmPVezmK2ZHm6IIl7hCri3oO7hJHk4oVNwlBR1zUhATsXLrK1fQmR9qhUTJ5lBMLiWkMo1knzXUbrJfP6Hi6MicKep9bXLfWyAmsYDPtY0+N0MiZWMWGWYWxL5mqcclhjyLMeu+cj6rJiMhtTL/0hLExitJ4RBo5er09/bYgYCKqiwRloQgmDIbJNCeslzWLG3UfHbAMaQzUvyXoD+mkGQqCdIkRBIM9AjJ4Xw3t9Cvm2w2HRrkCJVdqFwKzg708c6FeqHOsahPXpDrLj4Aj5uPNiV5rps7XeM3FWr1pSoXYbBr0N3ry15AOXIk4bxyDbpVjsk/QSDh+NaVtFYypcAI3QqEgRA1r4TaCwPkVF6JZx0ZCEAbFatQ8i+mlM61oMFtNokiSEIMZYz3Uw1tEsFmysb3NQniIDqCvNV1Wf4Auv8rN/5s/y8me3MXGfxaLtZLFL8sEEa085t7WNAyLRYgJwvQEHbzygQNLb3kGLhEi2RKM+BBGD/og33niDaxcvUc+nZHGAyiLSno+nz/Ie9+894r//H36FL3z+D3jxox/h1n6JsZo4rrlwYY9+npJmhhe21tnZ3WZcVsR5j//9V34NmW2xOH1AKiAOAxwOrf364X3WXaSmE3gk+GNp7Du8+2c2pm9klrwXQwiHocU5n8YohAAjMfhYTf86b6+STj4uOjmJU16JZw1nChEb4iHGQARoWoRyICTa+cRKb+GRSOMbCM4JTDDAqQorNEiHMQpncvrB93Hzyotsbn+VRliOplOeuvIc/96//+9w6fIe0pZIleGswAXwo3/yx9jf32dza50gCFib+g3/5etP8eGPPMNkMqHXy6mEY/fGdf7Vp67x6U9/migKePbZZ9la72OrilfuvI0Qjt3zl1gUNWXRsHNxl09+8pN87nOfIwq9vRkjuXHlKW7cuMGVZz/AzrkLaG0JQ8G92tDYse/EuQZESysdIaZblixSik5x0iIJCGXYJXSEKOmbD1YbmqbyvAuVEAiNFZKqWqBtTSwdWINQASJMCKQHz0oZ+B64ANF4KZToinbGGFpd0piSsq1YNBOWekHbFhi3wIkSId9beKcQwjfSuvUt8m19b8VwoJSkak6QYYgzbZdVYKjaChvMKFzACxsvEbseZXPMvn6Vt9VXMGHFurvChf4VRFGzHBywNKfko5S7r73JYlJxumxZzB/SuJarO3s8te0PgWXrIbva1IxnLa1zRIEgkpYkTri0u8PFixfZGK2jm4qTo5bBuUtUQiFSMM6RBBbdNsjiiKOlIct7BPdfhX6PttxBmpYq6xOoEN22XTGuoSyXSOuo7n4VF4bUd16hWTxCNyVmcYKjxeBQMsYKiUUxOZ4yHA7Zv/Ul4niECiOK6QlZLycKM5atgSjBEHA6842fOEjZ2exxbi0lCD3D0miotaNoGqZFzfGyQBpoXU2IYDTosxW1VHqNnaZFi5C2bkCEfOS5G7Ra8PrbX4dbt3l47zZX964yG0+5nGbsPHWV5aPXeObFT8DXC5QMONCPqNsCHTqUXVCXizM2EsJ6YOl7aDddFbe7f53NTx9N7F+rlEK3BqkgDANWGIjZbIGSIWEYUdctk8mMZVlSVg1F7ZXdjfYKhrWhDzIJcER5BsDkcOoTuaVP5vZfzyc7OeDi9hbPP3OT2mpu337LM3eWSxrj56o1YFqLCwzWaKSM0LpBxSnGeYh+IDvshOhYS8YgpUKoGG0MKlDEvS7j0zS4KPfFGXyBpnGS1jmQCU4F+Hw9R9islDiSxzZii279jt9zvgSN8wD3NI7ZG3iF/3Ctz+72Bh+8eQ1hLePTY+YnU/+7jq97Pmq/T5DEyDAgTjO0c2c22qqqaI31Chz57Q/Oq2vqr/dj9UPoBM44AmuRwvH09SsY07K/f8DlSxcoN/p8Rx6Z93is7Dffzftb3Xq1l+32pg0o5/jQsy8QEjEarLE8nTAcDli4GTQNsioxzpIPByRJzHB9RJgkqDAiiVNOj4+YzyaEoaJpHFU571hxFbfv3KFoDFVVUzQt06LhtGgwVUMc+L3Uei/zorGOlyuCAO0sra6Ik3MM1vpnhSEjoG4rjo4fsP/wCCOgaTRt14G4d7CPdY7GGYqqIQxDZKB8EA+w6MQLptUQ+tTXuFez9mCfl199lZ/86T/N3/0H/w+//48/R31cEQjJ2toabV1x//5dds9tcfhgHxEGHO0/4vzTT5OmMSts0qof5ufLtxYvKGO87K4bhsdw/1XC3KqY/E+yQ/zu7VTCd4iN8ylOWZ6wvrGGwkM6ndEoFNZ01TmN38DpGqM1SeYLGyIIsc5DjIR0LBY1dVMTy4A0TdG6YTI9BedtQULUJHlG2pRUdeFZN0IxHk9JjCDIcqxuKcslRWWIZE5dzajbivHkAVZXRNISqJRsFBAEA8DLGC2SOIjo5wNOwuOuqiewWtNqSyDxCQ+BRESCpmlYLAr6wx5VXaGrBpWFIB1tU3exoqa7OBLnNGXVoCKH6iLSnPNMGcBLn60ljkOiKKE1FQhHU5QY57sAjTU4KdjY3EQIwXI5xyLI8z5VLUjijDTrEYYtU9sgzIK2qpnNl34z7rzdazKZEHUdzlAFqFBhraZtPXhKCO/tVUp5hVQY0bQ+Mcxai1HegrW5tc58vqQoCi/R/CeYhH+kIfC8gE7R5Q9XXWW6sz4I6+Ozz6TNShFFCUU9o20NGkXcH7IWxszqipPjOUHb+qS0xtAfDpAy9DJVY5lWczKZ0Bv2kCqgNZ4E31aaZa1xrcbpBluXZIMEEXnFhQv8ySkMYnAGqS2R8j5VIUOqWoO2KBRN67kxSZKwNuwTyZCi8NyZIIwwThOpiP5oRJpF3nooQsIg9QXWLgxHSEmIwCExSiFEgHa+MxSnGSqMcW7V7XCEYUjkFMuyYthfI1CJ97xqjZIGESiUkygVEMcJUgSUdUXTVNS6xtZ+A9NUBTqMcUFMng/QlWFZVyybiizv4zDUugAlmVdLtDWURcN8PicbjLqHbgfBRCFxHYKkU7m8577l72xYYZFixRN5p1LjGzsnT26IgHczVsQ75d3v/okdFi01MtW0SYsWMWHSAwsS5cHIUUCr7dnhKZQ+Gcu2DbqLNffrlE/sS8IIJejYHc7HHQtvnRPKJ5hEUYKQ/sHpff4aa1psENC0NVkUYi3E0RaPDsfkw5CdC2vsH2q21gdnCsOH+4dn3v21tSEb0TrF4TGLxYIsy1hUU6Io4uH+EddufIDGOm+ttYI4y5jPJiglzhRsqzXLWcug32cyOWVWTrl77z7LpkEpQYxlOZ+g6wW6znnjrQTdtgRJwnw8oagdz37gJvdvtQjp7b2rBBd/SR+Dgt9hoXq8DT6zXa32gp0A630bq7kGoJ1G4XBOYsXjxKnHpEgJGKRTXWQsONGt6090KM8KU93fpZAIK975NgFSiO4zSwTy7L7tZyPCMOaLX/yiT7EIxhwfn/Kpn/k4V69eBqFJ0hTdPi5+9XpexWVqTZIkXjHatmdqV619clPYPU+ldFy/dg0pJffu1yMxhQAAIABJREFU3mV9+AxxmjCbzdC64alnn6dsTlgul+T9HltbGzz33HO8+rXXyfOcp59+ml5/SNXUHB0dYjpl62Dg1bRBmHAGIVcCpQ1CeRbN6noL6bpI3JX6RoDq7uuua251i1MOgcK03pqqowrhHLWsEVIhlPUxx9J2xV4DHURauFUilfEwY6vRukHrGm0qtKkxtnwn4Pg7TXP5Hg/hIKALHOgsH1KAcC0EXiJPxxvQokW6gKmdcz65yaIZY9oFIHkoHrIbPce6HLEWXKI0S6yZo9wJUdjnYPkmdWtZVBVHi5adXkYah8znC1SniqibhkSFJEnGTuCo2wbhYGOtTxpmJElK1RjKtqVuHdVyglYJvSztimCSYj7FNHMIQgLZMh3fJ8gvkgU71Ee3Cfpb3m6qu8K8jKGpQBvaxQm2mtHMNdXsgPGDr2NaTRS2OBUxny1wUrEoNCLI2dgccvroIafTUwL9iKy/wfbOBoP1bR9iESfMZ8fMFoWPvLcOqTSTqT8sSynRVpEEIePZklZDbUoClbKsG4xu2TiXcLqccO9Oy6zU5FmCti1WG4yF5WLCM1fO8yPf/yL377xJrWtevXWbD770Aqf1hH5ZMdhcZ3z7Hhtr54gY8JXZWyAtgREYJJ15E+kC3Nk0/KfreS6EwDqN1ZCmPt2zLMvOphGxXCyoa588a7vb2jiHRXQ2qtAfKnEE3eF7Fcds7eM6hBW+mL4aaRTTy3OUaRHSddYrexYKo7rk3NWJzTmDdN16944foLNqdr9g54R3Pfjthl9DnAERYs4+sLNzIjoFtOjsf53Sp/PNvlsN4i1OXinuv1a3j4xjnJBnLBMVBoQqB2fRpiENEv8zRSFBFBKmqVf3BGG3j3+MgzBPqm++w4fpSsXz5N5LYInwyqGmLNja3CTPcw4Pjzg+OqW9tMv7cIr5lkXMJws3302hUwiBVKZTyHbP8cYzWD/8oRe9Grptubi3S7GcMncL1gYSbUJ/BowC0jhBxRFRnuIQKAemrAmtJZWSuqmxbY1dltTzOeOTCVE2QGv1/1P3ZjGWZ/d93+cs//WuVbf26u6Znu7ZODPcSVmiaFsWbUB2FL/ESAJEghMgcl6CAFHyEMDIYx4SIMhLkqcA2WAB2WDkwY7kCJIihhQtkuIsnIXTy0yvtd/9/rez5OH8b3XPkJJIJhnSByhU9a3bt+5y/uec3/f3Xait4N7xMUsihGlwqxndvMP1K8/imhUgQo3jDVWjmRcFRWNIez2ECFKozNZk/T6L+i5HkxlRJ2e+KDkdz3B1SekcOg1+ttIHdt1yviBKcrSMSFXGYNjBUFKZsE83ziKTqJ1Pkr/+V/8a9z/8gNVswWZ/yO7WLnfuvk+cRZTFgjvvvcn+9g5RYfj0zRfJohjb61PXAk0w6//LQL/US7z0l2EnTXttOrU+R3q88MFuQoin/K4uP/kf6zP/iUGcxgZttW0sWb/P9tYuW6OdEBXpLEiFsQ6nBHmSY3SBM56qNhhn0E0EKWgBiAiPoWkqLi4umE7GAMQiGEgmccZ8PmexWARvkH4fa4IHz+R8QnFkKFclsYXHQrAyJV4F+rVyEWVRczE+o65CLn2cepKkh5UP6Ixu0+vkDPKIQX9ElnXYHm6yvWc5Oz7i9OgY4R2bm1tsDjrEaYTwFlM56rJkupwQS8VqWdLvxUTKMZ2Og7FtnGMag2ksS1NQVQ0AO91dojSirAuQ6eUi52wTEpKA09NjGgwyVsybGctFwbIu6Hf6JElGlg6YFw3VylJYSxRrtoZbbPSH4B3nFxMePH7EqiqpVhWz2QwpQ1x4XTf0ep1Q/CSaqqlZLQqUEoySUfAOiOLg2dNGTW9vb9NUNbP5edDwqgAmPPvMc9hacHExxlmoPmFqLKyR9vDz0xfA2s8jdETbi8UJVBwRe818YlksC6qqaA2QFQc7V1A8DmypsubxyQO8vsKou0PV1FTWBAaVccE0bFVcdmls01CWAVysVwukMfR3DoiTDlGkSLMI4Q06kjS2phGe/sYwpKNZw8X5OdJDFkd4KTFeEmU5N178FErGFIViuSixH96ntis63ZidvV22Nzdx1mNtKHiq0hJlHicNs9mMJsqJtUZGCicFCMXhwXWuXnmOLO1S1W0KGwYpW4NTaxj2RlT1kvH5MU0bZ9/Pc1wEVQMCRZoM2NlO0CpmOp1SlRVKauZFibEzGuvZ3BzSGWgq1yDrilgPiOKU5OAq9cYILWE6nfBodsR4PObq9ZthE1eyPe7bSwaKWm9qP19nvsvhRZCchAabwBHYYU8/X+fXEdXBjyagbe6y+F0fIiT68r7BA+ejNGLnHUtbIVjSvT7iw+MCoTvsD1UA16OYOO9gqyWrpqYsy2D4LWT4OU6faPJVSNiLhUAqgRKgpKBYLUjTHGsaIqWDeWGk8daRxxFaWorI0XhQ2iEdOGeIk5zjiWCw0+Xt17/N9taA/+n/+CNevLLdJuD1sfOSJPHkPcN4OuPi4oJXPvM54rzDnTtHjLZ2uHP3Hs88/wqT2RyShN3hJjrPGQ03GPU7GEQwd1cRzhkePnjIsweKJIp46cUbwX9sWWG8oSoblFRMP3zExrDP6+9e8NaffZfD7W1kmwC4tX9IlCqu7vaCMWWah86l0EAAiqUITBzP+gDeGqnzBMCQMhjzPn3bJw3keA/G2lbIE56DpQz+S16180/gnW0P9R7Rdox0+92tY6zDP1g30P36dX38e/sV2JGtF8NT3W4hFFJEZHrEtYMbpNc2+cM/+Cbd/g7/9X/wH/Krv/o1GrOiP+hSVUuiKGsLGEnTVIxGIz64dZvz81MODw+Zz+dkrc/GxsYGTVMznS45Pz+nm3X53Kc/zWQy4d133+X23Q9R3qGihPlyyTe++S0OdrY5Px/zuS9+gaOjI7xteOnFF5hMp6R5yq/8rb9Jp9Mh63XwTtM0FqUikiSiTBTF4pSmCeloiQrmz6FxE0DWwLJzsJaXCU+kRDDd9xbhG5wrg0y0MVhVIoWidA1GayJTg6mQOkJGJbqVPqgoRLCGIioUK8Y11E0VpLH1jKKasKoWFNUZZTPDNAWNnWNcAbL55IFFJUlEC7aK0NwK62TdFpaSJOrjRUQS75A7Rab6eJtQmApTHrMo5jxMzrkevcaO3OFRfYeyWRAvFmgj6Gxk6ERzffUi1WHBZjpk8PghD07PqZzm7qTAo8kj2NlMMbEj9gKZCHqdEVv9IbPVjFJIJsdjPre9Ryosq/FjVkXD+OKMbDBAEJosg42czc0hbrXEJg5Rrnj9m/8Lo819kq1DevsvM3j+lxBJStTfxNmGZj5h9fgHzMcnTN75vxmfPma5PCeKEjY2BsT5JhdnH9DtbfP9H9whzns0dcGqrrm4uCDrdEmEYHx+hhWCB/ceBk8hIdBesTvo8uBkgZGKi0VBJ9LkeYoUCVI6kIb9zQwvFUJuBJDfG/74nft8890FtQlgWhZH6KqiK2F71ENJT6oFx+ePGU9O6Pc6vPTKq9y4cZ3+cBR8Xojw3qKzFRdHF2yPnuHvXv9b/M57/wipPNbHOOuR3uNafybxc2BCsi6W1wW/lJIkCdfXarW6BIyztIv3cHJyxmpZ0NQOqSO0jtHaYoWgk3eIVAB2syhBecfFfH6ZooMMMiqeAnJUewxYzaZBfpWlbA6GJD6s3SF9JyLSIZTDuxBU4NrPU7jQbIEnDIEgz2p9tYAA2Qf/qeCHJ9sghTVLQD4lvQ1dQGEEeg2AqEtkGkeQjV/6cLVgk8ahVHx5u4g0Os2omgZvmxCkgKOzvYUaesRTLGXXJgXBE78TCJ6gdV3/xEc+23oPJUl0WXvtj/oMYk9Kw9npMa+8/AJ86iXe/cEt3nv/Fn/lC59ppfv/4o+PM3/bkzRf/oUvMjt6hK8qskRz7do1ykdwZe8Kxt6nqhpGG1sopRh0e8Q6IlYanL9k20dVhooTTFWzXC4pqoZBr8e9o2P29g/5G7/4Vf7hf/Kfsn/9Olv9Prff+T7SVnzxC6+hvODdN75Lt9sliTVltWS/P6C3OSROMpI4AC6BRKFI8ozpfMby5ISL6YyN0TakiusHzzCrC965/T6NFygl6eZ9KqXwccy8Ablc0R8mbGxu0FhPUTY8uHefOEr5xV/8Rf7ev/qvcHBwwG/9O/+AbrfL7Vvv89b3v4dCEHvH9f4mnbTD47u3g09Ot8sj06BtkFn+OKwtJ8P5yOIvk6yceHKdIgMTT/xIAOfHHz9hxLhsu1+KWMZcuXKFq9cOUUqFNCcP2gus55IqKaVCRJI4iZBWoiN5iVh7L5jPgs5uHT1urWU2nYVYryxBqjApi6JoE089VR1ofBEeKYLnjHNQNAWr5YxmsWJxNkZaizSW7a4ginyIdY5qxqua+Qdj5tZR9XPmw13S7gbZizFZ1CVPewhxQqIjdkY9MA2z6YJ67lGpQulghjeeTOh1u2gFZTXDIRld2SPPc44eP2IymbUMly5bW5t0Oh0aU5GnHbSsqZsSrTVCSmbTKcV8ycwGxo31NUJ7kjQidwabRAghWc4LNvLwN01ZI4WjKWruvPse3ntmZkbjLXhFnud04xjvZehAVRXlqgpyONHm5ERRYAsJKFcFJQXrCToYpPQ6feq6Ju9ELJdFMGlWwTel0zsNaWXeI2X608/Cn3YIgb9s6cj2Yml7vjJQQdeGzU4EP6LpxQWrqibJE5xLKStHaSouphMckjhNUHGCjmNmizm7hwdYHzry/X4/dNd8eKy6rnG2RumYtJOyKBYUdUU3j0l6geocpRFSeVIdQz9BJpruRp+rz1yj1+vhTLhOTG0wSUTa7VG5BuUMw80h3WxAGm9yfj4NxnEfNKRdxaC/QdbpAxJnFZ3BJlXjUDL41MwXK6rEEQlJP0u4eu1Z4jQhiTv0NkY4r6lNMNyOMs3JyQmTyYQ7P7jNBx9+SF02CF2HhLPdG2xvjkiTDtujLegOgiwASxxnDIcaY2qEEEznS5pyhjGG+VYfFQuG2zmdbsLF+RxjJfs7O6gRxDrizbe/x2pZ0e0MEMYzvgiJdFEUhW52221aL+7wVNzkz83wIAxeSlx7qA6Fm7w8YMGTzTUANuvi+on/zRqdl+4pyUNLv778S61hXU6CiVaM3Qm120Cfn7F/8CyLecOyLjGNw5SGVctSUFIGOnES0/gnh9coiqiqCiEDhVkqiHVMUzs6WR4kOFjyNAvKNilJtEQJi9IJVWSJhWZsLVVVITuKR7MZvbni+2/eI9u/QX84IMq6lMYxXZbsdLp0+hu88fY7PH/jOs8cHnDnzh2SrIfQEReTCS+8/ClOx0uq2vHFz32eZLiJEZ6L8RmqXiHyfjDZ9hAnKdeevY5fFmRZxt/5ta/R6XT47//H/5XaVXhvmS0F1hmW5xdEkWDSVNQnpxzuDlnMz3g4PkNLyD77InkWU7KObA1ApWi7mIggl1uDOEI8KQDWRYDU6iOf9Sctp3I4arcMbAfWAGGDQ+GcuvQkUd4jhGz13evqwqKlQEpN1Sa5KCTGB++xdWz2E1+Wj5rsrkEr71yQTKw7omi0jjncucndWw94/wff4MbNm/w3/+3v8PDhI5xv0EqyXM7pdDrtXh+KGOcs8/m8ZcI4Li7O6PV6nJ+f0u10GI/H3Lp1i729HZ577jmOHjxkNh0z6Pc4PNjjg4ePUN4xOT4l76RcvXqNYjZhtDnk7q3bfOrlT3Hl4JC8G8zh9w6vsLG5SdbtUTdLdCRJ0xwIbFwdwcZ0l0I3mNkShaMWFmMN1jasWVnIIAX13oFyOKnwSoE3eBU8BLwTIBZQB7BQ2CVWalyzwhY5UkVInSLagIKwNwUmrW1ZqI2tKeqwN6+qMcvijFW1YFmcUNRz8IbGzbE+pFV9kkMIQSSSYJRNm/DmHd43eOcRIkjGjTVIVbOsPmTlYlxjSTtdOmoDV854dvAqudvBScsH5k360YiCFaKfY5MOO2eb+JP3WDTw4kuvUBQFryxvcr5Y0dias+k5x8dTyrJEEZjeWxtdBp2cKEno9/tcT67itWQw6NGLJJGKeff2+8znFV5r7EWBkpDHhivPfpGLRUHXGaxPuX90gYw7HJ2fcufb3+aXf23Iovwm/WdfYLO3RW0l87vvcPbgfZaTE17/s+9SlDNuXjnkyjM3qeoVR9MFJxcFI+25P54SX1xwsLHFvbsrJkvD49URg0jR76Y8OjrmYNhne2uTbnfAqliQphl5dwQ4rnYT4jZdbrWckMcR3W6fZVUyWxV8+PiY5Srss5+90qPf3+DhpGBWLLl/UnKyEiylYHy6IhaC3Y2YQeZ59dM3EVIjvKPC8XZ1m0Uz5bp/DtlEPJifs5ts8OjxO0RK8evX/iVen7/Le6d30B6scAgiRMjVvmQ1/qzGx01G12u49575fN6GkmhmswXz+YL5fBmiw8WS5aNTyqpuU9aCd2XazehkObGW1KsV07YJXbSpVJfE27Viui3qyuWCPE3o72xzcHDAg1u3A8MQ0fphNqQuufTxWbPwnm5krs3TAWIHxgW/Pi9l8MnxnnUbSQkJ5sk5av0QiYxCF8C6y73DqDbNUIqgjPAWb02Q5UqJ8MFI3cvk0jdNChBaoaXEoJECjKmJtMRK25592vOOd6g2FTNNg09nWYbUo6YxSNHK4Jz7sRRP4TN88pn2+12+8LnPs50pxqcPOD87paoqtrdH7O3t8fD+fY6PTz7iZfcv8gjz1142WaSQ2LphZ2eH8/sfkqQR3TRhoy64rj15knLzuecp64YHj+/RGDg8OKCbZpdNviTOUVmHKOsTxyn9xZxOt09ZlowWSz77+S/S7fXpDoZ8+TOf4tHZCY/vPuJge8CVnR2kMSyXcxbzMXu7I9I8Y7Ga0+l32NrbDyzJOICnrJZ4JamqgpPzI4qyJukM6Aw2sDLh1S99gdffeIva3GOwtcnJyQmPHz/m0y++iG8iTk7PmSYV3XPDCy+9ShQl9Hodlssl737/dRaLOb3RiC996Ut85tOfI1ICsyxYjBcMejmf/9yniZTmzde/x599+03+fRPiRyKvWoZxyzH2/IUenX/er9bXPIAmNHa8dT+1tPQnZOKEw4kSkiRN2d7eZmNjA1M3GN9cXvTBRNDSlBV1bUCYttsXOnVCBxr/alWwXJXYxhBFEWkSjHmbJsiLyrImzTRxHJPnOc5B03aWkyRBRxIiQSQDMlYLgxeOZjqlKxrSWNLvR2xsQBaF+D2hUuaV4mIa0n4yWzGfntGUDfXh1VZCIxkMBmgZgI5lucQ4sDEIA2maopMEVwb/mKZlrURZjtQxAFVZUxchySVNY9IopipLrDcowLfvkRCCuigufxZS0TjLclWQWo1WisbW2NKhdETSaTta3pEkCcNhH6xjvhgHU9phn8I2lKYBZ4ljhWnCe1nXBWVZk6QpcRQRx/ElVd7ZkDIklKSbdwL6H0WXEyvPc6qqueydSB3cxYWU2Noitf2Z0BH/vCGEwBp7WUAFt/SS6WIOZUMWR1Ttoq21prEmpE0IgYo1WZ4SJwnWhyJYK4GOg/v+Os2pqipMU5Ikjqw7IE1TXJPQ7w0C0KAkUgRdtcPihSbKMgYqAGxehMeSWuCNxJgG4xS6jTv3IqbxwWVdpxnDjREPHiYoqZCRRqLwKibSCTrOqG2FcB7hg3u6axxGB7p+bzAMm6tKiJI8eFlIwDuWyyV37t7i5OExb7zxFh/cvQfCESego4iNRzN2t7bpdrvcvP4C4tCTxjFRLBBeooRExhHeB1CgacLcLlYVidLEkSJNIyJVgBTEcUIUaQ4O97h/f0RZuBCbbSzzeYjo1LpNPnp6TrVJEPzcgTg8YeH8GOOjEqvWKf5pOdXHIhh+ZKJG49Ba4qiJ4phiXlBbRxRH5J0eWZahdQFt4lTkBc47tNQ0jbv061lLqtIoxllDJBVxrNHEKC1QCIyxqCQ8p8tDr3BIqRHCoRF0k0BtreuafKPHrDjl7HxCLC6wpsaacBDsdHo0jeX4+JTRaETTpqIhYxq7YDja49r1G8yXFfXxhO3tfW7dusPmYUPa6yBbz5HGmEvT27pWlw2A5XIRWJxJwsHBAXcf38VaMM4Sxyl1XdNYRx7nJFnC6fiCfjcnSmKq5Qy8Y3tri/LyLf8YCPeUnGr9fjz983qPW9//ZwHiBMV1E1ghop1bInRYPWC8RPn1K3N4YWlJwuF1IFpT3SdsnMCofwLa/KjxkYSXp24Lpr9hbzs/m/ILn/sqG71dDp+5xvu37jEc5HQ6ORAkmWVZXnZFnQvS0vF4zHK5vJQYRFF0ORcPDw8ZjTZ5443vUVUVqQ57171795hOp2RZBqYJ4JA3FHXFaLSBMY7Fckljam7evMmDo2MODg7YOzigFmE/7PZSlMyJdEZVNVRVkFUkWYorE7wQl+yky9fr1/5dIYkpdKdFkFAJjZRrGWsAe71zSB9ANGM0XlmoyrZwqhFRSGWRUqIFrTeODaCQ9xhbt19NK6cyYS8xDdY2SGlaltDPSErVruOiNdQOHC0LokFIiRcOqVxgGmhHLERgiViIox66LllEBSfVY571r9BPryCtQESCfXWDuBEcT74dEtdMzelkQV1MKVZT9nb20RKefeYqJ49OWCwWzFcFCh8ST6Wi2+/hGonzFa5smBtD6aGxwcTWGMP4fMbGaINVveRkteTKc88S55scnzyg9J7NrR0uLi4QWlKWFUhJPT8llq/hkMEz0HvmswuaxTkn85rDrS02967Q4JjPK964cw/nKy5W9yiXNYfbOXu9hJs3RxSFZ7kyfPv9e6yqgs3+LlmnCxbiSFGsHP1eytlsRtEYHh/NME1Jlkp2drZJsoSz+YLHxxMar1isLNZoVo3jdLrioBTsb+ZcjB29LEVGksfnUxohWALPHG5yupjzxtt3Gex26Q1yTt9YMExydF9j9z2+qjBRxbJecbC9x+TufUg1Lw9v8vajH4TCXko8JmSK/BycF5/2M1uv1da6S/uA9W3L5TLsUxDCQmpzGVeuVIQWgtpUSBkSloR/Eg0erkcQEX9uZedcSBAa9Pr0+/32tnC9WuufRKN/fG39c/w0BC40HD7CUA9G66FhZNH2SYMz3MG13l2BOSA9KCGx4oc/qY/vAZcMnDUbVYjgQoBv2fsqsBW1QDv59H8Mz6GpLx9zLT8LDWJ+6L5/2Vgn+a4fL8sydkZbbOYCsxpjykkwxY0Tsiy7BOx+XqX6P9logwfa1NCw+xP2X0HYu6KINFIhyXYuMWWBKSvAo4VGKtjoD9BSIYXEGYPWEpQm9p5ut4vWkqiVp56cnJB3g8G61pq/93f/DrNmxeP7j6jmcyKdUBZLinmIA8/znCxLGW1skXcGSBXmBiJBtGdSKYOPWlk1VI0h9p7GOobDAYcH17j7wX2Wi0XwlGs8SRSxsbFNrCTT80lI43MCWvWxBJyxlHXF+OKC/avP4Bz0+31GGwOWkwu6acz25gZ1UfLww3s0VUlTNpjaol1rj6AUygT7hL8sZMX5kAQLYc92rBu4YazXFhdOCT/2/P74+IlAnMu8czxxrHn2+jWeeeYaVkK1qto47bW9raRYzFnMp8g2WjRJUuI4xtYNtfXMF2Nm00mg88cxcRQH6U+WAbCazplO5mit2d7eZT6fMh4XRFFEp9dDasX2aIt+p89ssqB3nuGakrdO7/Li9SH7/YQXn9mk2w36ytIqykbx4YM5VbdHJBTu/ILbZcHZ4pgHb32La698hlTBYDBARZoSsCpCxoo4CQR1V5kQkdfp0+92MLaiMQWCiNPTU6pVxeTsFKEFo+EW/W6PyTzImtI0plnOEUIQZynOOe7du0e/36fXC7G2pizxtWdVLlBOcH5yTH+0QyQ1dVMSR4pOJ2Wr10NpGMQZB1t9mjrI2R6cPKKpPKaumDcrpNRkUYLwOcY45vM5xWp1efEYZ+gO+pimQgnBaLSN1pJiWRIhSbQO0eydHNMELyThw2bW7XZZrUpMW6R9YsMH2qRoCw/PelNri6a1X0A7Z4US6CQmizMWyxllHWLRV2VgeOX9DovlEuMdw16HtJ+TpyFGXsdRuPCsw1hLWZbkadTGSEYt4CDY2trCbw3J8xwVpTgfuiidpIeSAnTE7uE1TFWjVIZpwM4rTOmxztI0BXVdolREZ5hR14ZZZXHTCVIqVBxdbjjVqkIOs+CHkw6ojQQRsyhKEhXYA04Jkjgm6mQoD6M4pa5CV1zIlKUrODk75h//4/+Z3//df8JqtSJSaZvgYJnOSyyWh49mfM/USCm5uneNL3/5i1y/eoWXPvUSUeRwLoC1Unh63Rhr4nCde4k3GmSO9Q0ehzOOk+Mjut0uB/uHHBw+g/Oa44tjKtNQ2xrWhbAPGzrCoVX8c8jAeTLkZZHLUxY+QWJ6WeBdeqUEBg4ehBOXxfETQOBJsSWEwMgnmvMACDpUKrC1A1GxGj5EiSHH752xeVXz4fiErazPSbri4cqQaoOTjsoYvNCkFFivMA6Mq9nME6QUZGkWYpN9cFNqyoI4CpTxajEmTSL63YzlckneHXJ8fHzJmnBWgNR4Z7gWeUrT59FKcbVaEjnLzv4W3W6OsRWPHj5CCMWnXr7JBw8fcFE0/Gv/xm+SZClvvfk2j44fc3Y+IUl7VPWczeE+r750nXmxIs263H7/Fv1BF+E8sY5QpCQ64rRaYGY1s/Ep1/Y2GaaWv/Lyq3SyhCRP+Cd/+Ef00i5SeFKlSLUmSoakacrb83P+rc9+moPXbiJP51Rx6zegHFIG5uI6kUAI1W7qhEjrtjiWSiG1Qkk+cpgV8hNm4jio2uZJmJVtsSFdOKSJGEUCIhzSQtJImKde1lipERJkBLQR40gXGjHe0rSpSI2zuFbSY0XwUmisofIeC1hlcCYnixI6yQ6He6/x0pW/yuTC8OyNl/mFr/wytV0S64imCqwAZyxaqvbxBZUTFPMVjamQxjE5CuxvBmJ5AAAgAElEQVTPTp5zPhmzvTliMp3irGVzc5vlckmS5ty9e5v333+fvZ1tNgddkk6Xb759ymw2Q6cZ+y+8wM72iEcnJ/Q2dzAq4Ze++tc5Pj5GxTEHm1vUdY1xQT5a1gXGWwyGs9NTNnoDurrkB4/fIE0sSgxp7JzKLgKrKLIYu4m3Di0iNBbtfJC1OEuzanBS0ZgSrTJioQJr1AqcE1TlnGYpsd5hIonujJAqIsu7yDiAD2gPXlA7waopsLahamYU1QmrakLZXFC3SaHS+9DRFvZpcuD/70MI0G0Cl8AhpMF6g2vB5bWc1AGJzklIWYgGtGc3v8ZsVbET9bhfvE+fAffk99kqDhlkO1wVn2J+dJ/HH77LBx+8izMN1aLg7be/T+MF0+mUWL5DmmjiPGOn36Xf67I7GoZYcWPBOlbTOZ6KmRVoEaEiOBx0qIzkYjwh0op/+de/xve+/R2WixBP/3t/+AfE2ZBhJ+HZ/T3QMbPKMZ8VnDc9orSPzrc5ev/7HAx2wTQcvfldpCtIBteoF/8n71WemzdSvvPmeww2Rrz9wRnFakFhMv71X3kFrTL0cIBdXLDR2+C1127y2dde5ehixa3bb9M4wdJ7lg8f0O/3iWRE7QLIWMmSNE/AOx48PkVGOWVtOB0XVI2lIME1nsZEaOlYNpbzWcONgx2GPU3lNaezAVKn3Lp9B12f8uLegN3dHZol7Kfb7F7d4L2dgqz21CqYz4q4gz2fclsaZmdHPH9whZ1sh1/Y+RTfPn+fRCkQAmPr/1cSgp9uLgavoPBzW0C1rFffnieDPN7RNBZvoakFzjUsVwWLZUGn12VWzFiUC1Ztkp9tgg/m4WiTXm9ApBTWwqKsOZ/OsdYhW0P5YCpPK/cJ7BNjHNM45t7phN7OIb0oh9pQlwWlrKiiilgLnI5xNsdbAdIF1o2oEbHAC4mXgsKKS2b8pTrINa23zUfZwJVe83LCkN5hbbvXqZAu3ADCrxOswmMgAqOuURLrHF5InPNE0mKbkkaEVGKERiuNUyLIrpNg8ixDnzt4LrnA4tFakuiIZr4kbQyL8ZQHE0Ocd5DLFZEw2EhivcKKNpnLW6QNgIUVhDURsNbgnWcj0dy4dsCz157hF/YFhVVEW1ucnkWYFXS7CS/ubDC7H/G97/+A5mePKf7I8XFfxb/4vk/0epfh8yJI/1bLKVpL0kTj64rNrRH9fp/ZZMoHt+9hBaGJl2Uc7u4/kbu1DClkjItSZKTpiSH9PEHrmA8/vMt0OqdYNWz2u6SxIunGNDdvMplc0CxWNFXNPE945eUXyLud4MOYZ2wd7hPlOR6NkwohofFBgtzpdEjSiChJ6eUZ5WKGy3r8X7/3u6zKgs+9+CLnFxNyBf3tXVLXkMUpNw92efxwymJRcnF6QncwJNYl0nuyKObx/XvcfOklsizh+RdfIGkKbnzlKzx37SpSOOrFgtMPHjA9n3Dl8BBja6QQuMYRxYonPoJ/8VgDNl4+6fy5p8yN140xKUS4tn7K+fcTe+IEI8yapmmCt0qsUEoQKY3xpl0Ug2tzQKnVZUR4HIfu2WrVSqNQKBUObOvOcCQjer0ekVQsZ/P2/8VkWYZS4WB8fnEaCrs4pdMfkHU62MZRFxHVakmM48Z+l+1uxHZXk6SKxmlEo1mVNQ8fnRLrhJ3BFmkWc70fsWPhztGE2fEDRvt7RIMBlbVIFVGZGVVdMsx7qFhhqxpTN7jEYZqGNM+QUlI0DYvFKjCQrCONnry9cfv+rJlGWacbkqtaE2FrLU3ThIOcCPSqtVQpVppOloGUzGZTEqXo9jJGOxt466hXcyQWJRxNbZBOEAXYFeeDphEFUSwYRkOED+hflsYQa4SALEkpW1f7xXyFVA5XWeJ2YTSt6RTe4Zxvae6OJI3bDe+TpWl/dFwuVwAfQTp96/eglGbQHaCMwdRzytUsMBDSlFVZ4r1nWRZUVUXWScF5rG2IdRpM1lxJ2NFCZ0WImCiKiZS+RKR13LJHhMJaT93UVMWSPO2ioxipIvobW5SrFY2XOC+xxlCWJQBNy/qRUhOCWR3OeharijiOUU6g44h6NmexWGGMDVppITGNxSFxjcGJCJTCAV5ppAzJbT6OcUUVqIGJ5OLigrfffptvfusbnE8maK1I45aJoVO6Og0JbZXFrwId9uT0EbdvvY2pFuzubrC/v41HsCqWNEKQKJAqyA1NXdNEMhSPBNqtqZvLtSOAYTn9jSGz5Ry8QIqWUrk2N25TnJwgFECXQd4/X8O1aT/huXtUC+cE9sb6+48yq3sC2Li2g//0Wh6Ag6cTNUJRHgx3BUhFoxx16ji5O2awOcAVGp00DDc6NI8nRKkmihSqXhBJj3PtNe0MCIiiBEQwz1YC8L4F4RxSaZwxRLEk1oLFZMru7i7T6ZR+3gleSJEmUhGV9xjnEVLjhKF0jlqETeyDOx8GSWk3Js8ynJW8d/cue3t7/MrXvobUiqKquHbtWc4uxlSV5/R8ynJ1xrQ0ZJsbbGxvk3ckL7/8MmVdIIWgKavAarOOajFltLnN9mjE0aNHfOGzr/BP/vd/xu7ONv/2b/0DbO25mMw4OzvjeHLMdDEh66bUk3Nc47j64j6Pz+5zkO8i3ROPhPVhRj3ldePlGsTxlwuOUB6pfWCmrQEc8bPo7wV2BlgQjsv4aiVwwobUOiED09S3IKJc+yWE9q2XPrD61HquBl8Xhw2HkKempaNNXWtZIsHo0yMIyYwikQjhmUwueBA9ptfZ44WXX8BaRxJ3MC3r5Gk2SwDfFd45XGNYzhfMzs6A0D1L05RulvP+O+/iBOzs7CCVotPt8tY77/Do3gdIPN1Mc+vWrcCmGI8py5L5fM6dO3foD8YMt7Y4Pz9HRwlbW1uX0oX1wdn79myCR7YHy3iwy9GH3yEWSxLl8fUMog5CLFBqCd6jZI71dWu+Hz0BaE2BdTGNIQCrlcHGlspVREozVB5rDQiJNSFAojL+0sDRywbnkgCKtJJoKzSVKTGmpjYzGrPAuhJPjadBWoXyIQ3ICccnW6sIpBbgRJArCIGUoLwKM6dlK6l1AqESbMV7ZHmHUkh62nFanQKa2lTkcYeD/osMokOK5ZSLD97n9PFdhp0BjfAIf8ruqEtlQpiGKWpEEhFLgbOSsmjo5DrI4uKIJEkwtWU5m2MJZ9tOliCijGZZkqYpL9y4xtn5kvceTuntXGN3c5/3v/F7pL0F0nvm8xVpIjk6OWMyL8nynMHmPrazB5NTVNrFVQVRp8fs6JTYT8l7Ozw6PuPo5AznI/LuBo2HKNvklSsHXBQxqwbev5jzxeub7Gwd8PVvfZO/8dVfJEkizs+GHJ9fEEdd5rMldV3T7fXI04THxyd0Oh3q5ZjaWRrjmZyf4jwYmVF5R1O6FqB3jPqKQR4R64ZuJ0UoQVdrDjb3iZKUZ/cHVMsVQhq63ZwyE7z74QnXDnf4nDzg3D9mNpmysTnAOMtKeLZlwrss6T5+xMN4yU6aIz1twa7RWof0mE94rNdl+Ghx/FGGi2gBnXDODcbpgizLLlkewFNWAkGZ0Ol0iNPk8jGrS1XBR5+D908MjgMbNphr3757hzhNWBUhKag2Dd5YvHUI5580inw4pztnkD4KrBPpEf6JjPfj/Jw1oOP9+lzMJUsgDMelTb/3f74W5PI1fHQVeZp1+lHZ+JPfP+2R9hEJ21OPua4tVlVJUZZP1Bs/4m+uX6f3rj1btaCUD/KXjV6Xw4N9tjcGSAmdKAlseXeOc4YoUuR5St7t8ODsGPcziBj/ccZflE718fcksKNbIG99n9ajFhdqOCUllXWYsmS6mLKcL+h2u6G+XMwZ9Pqh7laaxgUP2vVnKbUikzFCeFQc4TxsbARfLGOmTC/GLMoZdhz8Fn1jwFukCKbXT9f7gdiRIZVG6giEu/Qz8k4QJzqwJZ1g93APjCKJBLUp2ep1ONgbsVyFhKy6rMBCqgVSS5JIkySdcLZ1gQWaxTHWemaTcQj9qRZcffYZ7r31BleeD0EIk4tT5qbmxo3rdHTM2cUcZwGhaGpLEsN6nj0ti/pR43KnbRlpT25/6rPzT9Jof1pLpp/QE0fgvcWYhrquqesKvCTVETK1GCPRuk+5XAVJhQsLdhzHdLpZKy2xeCzOeZwPaRNpmtI0DdPxGKRka3tEmgbEz5gabyzOWeI4bj/4hMo0yDijt7FNpmPMfEEsK4ydcXM/4tXrPYaxIklgXDhc1Gda59w6P+JP7i7Y32hwSc5eojnsCrSWDKOGB9O7FHLBtc9/lUJoThcVg9EW1pR0s5CEscLQz7ooITF1QzYaMhj0ORpfMJ0scSZEqvaGA5x1jC8uiNMATi2LAoA0jmjKiqJqGG3tUMxnzKYLtjaHJFFOY8Y4KzA+FFeT4zNINDt7e0TdlNI0HB0doZTANSuqWeia28pRFwtiDcN+jhVdbG2wxlPXhijJL+maIKjbi9QLwIlQ8CuI4xTrS87HZwghyPtBLiSlxBjLYrVkVS4RBI8jG/3sFsCwwLQLuRNt4dLqlBuDUqFDvDHss9nv4dyK+/eXzGcrlqakNDVeaZJOTmkaHp2c0lhBp9flhc1N4jhC1ZbG2NBl1xbn65AIFSdorUJBh0DIiKzTZ7SxS1MLTh7NMIVnZWq0TNk7fJblcomUUYiJrDyrVUltS6JU0usOEd7i8IxnBdPpnEjn+Nox6vfY2NhA+YayLJnOVkQJ5CJHqAwhIzyCpjGgNUJFCCeomwAE4iXOlnhvmJ6f8I9+53/gj7/xR5wd3+P69WfJkpxUJ9R1TZIkbOz00VFCbVdMxzOKouD08SPeefd7PHjwA3Rk+dqv/jU2hjm+l4S4c90QJSkIQ10scZXA5wndrkaKmrqcY70i1hGPHj4kyzo890wHaw33j+4xW8xZrOZk3QSJJIrjdkOyNFWYY3Ga/Mzm2o8cwuNFOJCuPUMkIQ5inULVklqBJ4waALlewH0AZXy74K9/r6X+IQo1QEOFFholFKWuEL2SLIk5fVjTTffpbt1nP+7Qu53QWEuUxEgF1CsilSAROCfREiIlqZogeU2iCCUkvTxnNp9QlwW7m302t0Y09QqrBXu9DBYTvPf097Z4eHZGoqA3zDgZzzmZTDBI9EKTThb0hwMwGusUs3nJwcEe29u7vPj5V9nb22O+XFI1Df1+nw9OHnJ2PmUw2CDOhlx75gbZ3g4XZ+dcHJ9z99ZdXnjpJZarOb28w3RyTrHKOD895vrWgCyJWC5m7IwGDFLJzd/+Lf75n36H3/6P/yNKD6lOmZqSQSfF1/DF689z4+ozfOUrX+GM96kerThZzejGGSAR8qnDaZv6EJh+7UFYBk+C4IECSE8kQ+f1yaH2k10bvffUdQnCEgplcDKk4nkVtc/TYLxEtEWIXk/PNjkB5VC6tW+4fL0erMK5dv8WYAUYt45cB4PAInBCUBcz+v1NFotTqnJJk3uq1TP89r/395FEKBEzn0xxvm6bNGEfCr5mMlChPdTGUMwXnJ4c0+90cZHmm1//Ov2NIYnW5J0O8/mMew/uk2QZo+GQ5XnKyfEj3n79CBF32draYjQaYa1Fa83jx485v5jwmc3NyySU8XgcOpPLJePxOEgSI8l8McX5kPRnjGNanHJiI05PLJH6Ip1eQrV8BDqicR7bFFSVQEZVy2QKckalU7yLKZshD8djyqqmrgSNLkmUJ9Ulrz4bkymJEYa6Cul9K1fhylOU1mjbxaUZCEkskgD4y4jKBTnVykyozAzjVkhRoWhQLgAmwrmQWPIJzkXRdu1Fm6jlZRE6+7pd17xEohFCIqWhFBMKO2PiIpTReB/jvCAVKZWveS37Vbqmj61mjN97nSiTSJ2z9/J1qvmcR7duY3xMZRy//KWv0uskLIuKxqwYj6dYazk6nRFFEVkiKdwSGXmiCKrZEq1zpDU8Pn5MZTyfffUG2e5z/Gf/1X/HH77+Ida/RSfP+M1f+9vcun+bh2dznr8xYL5asqwk05Xhb//NX0f0RtRH79G/9iniToLxNf3NbeIIZD2h21EY0/Bndz9g1Ovw9e98F4DPvPbL/LNv/Ql/+sF3wvvXRgVvSvjf/uG/yZvv3kLjePn551mu3mSxrBgON1HaY5qSplxSG8ftO0fEkUT4Buk8u5t9hIfNzSEAZVlTuRCvq2UooEJy1wzvJHXhMZUmSRKEc9y8dgWNY7S7T5QN0bnk62+8xwtG8vy1l/i+vMuRXeCagiM7pykjDl465P1//gO2zIijTJCnPRpTBEDZ/fjsgv9v56N4wjBo5T8fl4FWVXX5FeS6DVpHpGlKUVRP9mNrQ3BEa78wHG7i8MwWC1arkslsHgrSVgGu2zm/DhRYNwiEENTO8M3v/ClvvvcOmdIkbdS8tR5fG3xp8YVB5rSWFh5kkMxY2yCcwmvRNhEF1n+s8H9qC1o/fckPN17XXiogP/L5BEP/J8bGa2kItCDDZWJe+z63f1KIIMle3z9Y1bjLPVOK4GPpmtAccN6zLEumszln03Hw0Yo0rqpDiID+KBtbqfB3TduA9t6Sak8kBM9d2eG5vS2ySGGaml5vSKcbWMdeOKy3bO/t8tzN6/zZ7/4Blcl+zFn0yY31fP1IStdT4yMJXGvptH+SlCkAhApAoJJoHeNczWwyZj4dU9fBoHjYDwCsFZJrzz5Dp5vhtcSVFitANI5YeZSSiCR5YhEhIckztvU+W1s7TCdz1IViWgWFjWgcSkqSrMtwEHxhVRSavL1BH5110DpG6ggBNKbBu+DhJ5zgysE+y6Kkm0ZsD7dQjSPOYyIVB4/QbUllDcvpjPmkwJmaRWz49Asv0N3okuc5TVNhTJBwOqk4Pz2mWK6ovOdLX/oy97//BsvlnOn4jIuzcx7d+5DPPP8Cv/Ebv8F//l/8l/QGQ5qqIckzLLY1/PZ/KZNwfY7/+DonfkiKGNYG9bH7yR9zffwpQBxPlmVkWXapS3fOYX1YmJypsa5pTU/jcHBQimJV41nh5oa8MySKJfXSsVqtUEoxHA4v9fB1XVMXJcvlkm43xynBbDy7/N3maMiot03VwOxsgUsUyivK+ZJmPufFa7vsbveJbMnFfMrw2ms889ovcer3OP6nf8yD33sdaR3X8oy//+/+Jjy+x+vf/i7Ls4ds7A6Yuwo3Pibuj9AiGD0aZ1lMV/SGG2xtD4P/wmxK6iJ0EoGUOBviUE1dYmwdzM1cgxeOunasFgHNdMJTNYb5fM5sNmNjOMJaT1FUPH58TFVaJrMlWxtdyvkSIwLK3ok7bA5GrMqGk9kRtalIswwpPKPBkCTO6aQalWq8s3hhkChQjtVqAYCxFZFOcHjKpkbpgOTPZjMGgwG9Tpc0C4Wz1zpIg4TEOEdTB8AnyxKUUkHuM59iGgt8tNj8JMaTKf7DSLXHhg0yUljj8bZByginBcONEYvFjAeP7qITiTAN55MTxrMLrPVsb++yOdoi7wQDxChSdLs5Z2dnSCXZ7A9QOiVJMpAJ1WpJVZac3j5mulwwLSoG/S0G3U1G/W3cYItOv0PSjUizAUp3idChUPEVUkoW0xWZSOikilVRUJQzlIwYDIZokQSJRFmQRZru4R4qiZnMZiSpR0RDpGoAg0IjvSSOErr9jChKcCiaRqC8b821a/7p7/8+3/jW16lNxWe//GWyKERYnh6dspiXWDxn0yWD4ZDrL+1x48Z1sjzh4vSYb//Jt1iMp7z33htsbfTY2dlhtDEM6HonJdIVVWURLkYIR7moKJcNi9UpdW3I0i5NNWfhHMPNIToKHdF+v8/Ozg6Dfh98kKFcbl4ysEl+VhG5f/HwhCjhtaBqrT9/Srf+FHX7I4ej9v7rAyWExf9SX+79E8lgy4jw3uJ0g7AabSRSWoyfU13rcXa7IrEWE5WksWV/p8Nb739AV43o94b8P8y9eYxl2X3f9znn3P2ttW+9TXfPyhlyyJFISqFkyVRsS6IgKJYhOzLkRHE2w38EQRAENpB/bCB/BAEMJM4fCRwbho3YsS3LtmRZohyZlLiI5HA4o1m7e3p6qe7a3/7e3c6SP8591T0kBYmONOQBClVdVeh6795zz/md7++7VPkUqUIG09yDtWHUJGqk/sAlPCir65p2FGGDgG4g6DiNCiRHxZCgzuhFhu3tbfb397m60UFbQ2WgvbPOxARYGVKXc/KFZmdtg1v7Z+hpzic++WE+9tKLjEdTBmcTBmcTNtZXOTk4pNVKWdm9RtLboNKGN965zevv3mRTW7pJxoWdbYR4wO33btFWCTNzhpIaigWzs1Puz0dsXgzY2r3A6toGr339S6y2M37yz/xJfv13vowuK8amIAQWZcHKSoc79+9wdnTIyatf5xf+z/+RN195k+hyRlDXgEQKz4YSQnqTfWRTgIJznmGCXEaLC5xwRFI2GnTxXZFTATir8ClJHhixzmIdSCewzsd+S4knMErPvLHCEsjQx14rC9Kh8IwRbX2X1FqLldqDjRL/fSfQOJwDKyVCBkigHW2Qzx2dbJfV3i7lIuUv/YW/iikFUmgmwxOUA6sUNvadZ68P96ktoNBVzXw04e6Nd4lDyXg0YDGfcuXyRRZVSZBJ8jwHbZgNBuyPRpSzEXGgSKOY73vpk8go5eDggE6ngxCCwWDAtWvXOBuMANjb22M8mZ0DPHVdg7beIy7AMx6jhDjpkmUxUbvLl9484cGow73jMZ12TEt9lFjMyeQtInlKFu+TWlChIlQpoVI4CYeLTYaLTb5xUNDKtonlCkm3gw1qjo7vMHj5HiudgF5Psb6aIAOF0yVTPfRBBPMCqTP/jEo/L42IKE2FdZVPt7JzHBqs7+I75VlVEovS+t+ft/3vMQSNUaxZyp79vJNEGNNIs0mQyvtfKKmACGSEDEMEAWVl6UeXWI8usBpeYX76Lq6ska6gmMzYuLDF65/9FYoSXvj4JwkJKPM509MjhsNjbFVQWk23naEt1F1JWVXIIGC2WCAWlkwpBtM5q6spQkEQxfRX27w1ivi3//If8fe/dIM4CGn3NlCtNv/bL/8yP//jP8uDO5/j1s0b3L4/JHQlLz3/Is/94J+mu3uZbG2HIPP+eFWuibobzI9uoqsJn3jxBVa7Gf/6q29TdOcMFjVbm9f52//in5HbgM985i+zuf0UcVzxq//i77C/f4ef+Bt/l7/5cz/I5c0+i3zE9kaXg9Njbu/fo9ttc2Fvh05rRjLXbHRrFoUmS1P21ldI0xTrNNurK1gjENLX3lkSIYUlrw2nZ2eMFiMvuRYCpCALIp68sksoBTKMGQ8e0ElnRMEGn/mRH+a1gxt87cZrbG9sst5P0KpmZmvOmDEaDhiZIYMTzep2nyAVaPx6JJTxtekHPJaJot/MGnnEvHMUReH9HxvJrDGGKE7J85LxaIpxjqqqUTi6WYsgjuj1+yC9f9w8LxiMhywqLxN6vAY4VxyE4fsUCChJZQyDyYxAwGY7QRuffiitILAQWKidg2VyoNUIGyKakAln8GEQgJXR+yLMH7sA5+9XmkeRsu6cJeybSfYxsOv9qU1LIKfhJkqFE14u6ZvtFmW83BbjPfgMvsHJElxoXph0zotLXNMEsJai1pwORjw4Oubg5JhFXWKdZ+No65Yvr3nNnvUJNNfRrzetwNFvpVy9sE0vggCNCkKiKCBNI5LUkwOMrdnYWic3NUn6ZcYPJv//J9gfw/hmL6Tfbyzn77cAB8v603jpmnCGeb5gNBrgDLRaLQgCTs7OkIGg2+3ipPcCc8LHkysFoXUEyjOrnfTWHYHyAUXKKYIkpL+yxd5lw9zNUDbAlAWmrCgWcwIlyNK239OcoDb+/JkGiQf6jE9ek8IRqohOmnBhe8efGTptNjt9olCcB304IM8LzHyB1BVxYNAYdrY3iLIWQRQShQJMQq0Xfo5LxcnxEff377F98TKXr1wiTVOK+Zy1tRW+9KUvsNpu81uf+xxZ2uZj3/dR2qt9TsdTVNbG5N7X04/HWfLf5r41vgrf7s5ZHp1aJUCTJPf++/4H3nLgOwRxltRB0ySexHFMHPvUhLquEQ7qqjqnS4qG9meWBonSI8V1ZRDy0Ztfmna1Wi0/2bTB4OlXWmvy3CcvtNtt8sdMgJ2tvIxIhsxmOdPBFFdqok6GcJ7KOM9nXFhdYffSRYpJCxGKhspocKJifa2NKbZI4xRlLf1EISvHyWxI1u7RDgIqVyJcBU4jVYQKU5BeRhWFinIxRwSqMf8t0VrS7bZRSj4CuYxpjHP9vxeznLKozwEUKSIiZZhOJuRFjQGq2lDWFXHSJk26dNMW8+mCh6eHTOYT8tIbEQdSMdvIiaOEzX6fbruFkyVFWRAELS+TUM1Hs3ksZ4yUgiCKvY602eCWEi8noCpyj+yrkDDw4E3SUEeTJEFbQW2NJ798l8bvR2vz196j9d5Y0lMMw9DL8ybTKWFoyPOc2WTazCtFlMS0ux3iOPZm2IEjIiBqzF2F9H5AURAyWSw4Ojzl5GCf119/3YM48wIlEzqtPntbV3jyylU293a4+uwVVBR59lRZU1deKhQFEWGUIFTguz+TOePZmJWVFYIgJBASbTW2fgQCtLMOIgAhI4wFGUmkE+SLOaFUhK7VXAOFQnkJnbEEwqGrBe+++w5Iy/beLqsb6yyGQ4o8ZzKeeQNP4xgMC85Gc1rrMdtbG2TtLu00ZD4esf/efap5xenxGUHDrEmigPGwxFrTRENGdNI+KmwjJSwWM6STqDZIBa00bjxvBGGkaDfpMGGUIASYpQeXaOIwlT84f68NBzhxbvvafHf5QCyLxPdTnB8Vj48MC88XbSVwGM/iaQxpv2XIJn7cKV9WOU0pa5xIKOuaIApIWhE7uyE37z08N4o2lSHuZjg7QyBQ0hdrQRAhGj18ICTGaNppCtaSzyestCStdsz21jpSWKJQMhqesr7WZ7ZYsNrrMZwuOB5OqVVGhStmERUAACAASURBVKVYLOgEgrTVI0pSur0uq+vrDEcjBoMRWLzEVjhCJSnynFavSzmcUtUV23u73H9wyN3797mwvokzNVprdnZ3KY/HhGnEeHxCCDzz5FO8d+sdpvMFuxcuoYRlPj7m3VdephJnXLl4iddv3iIKJF2ZMHUltbMcTOeciim/9Hf/Hp8dHNFr9xm7GVLGeBaLQ4rHDRuXQM5jhsdKPpJYCU+Vb5QjyO8CgPOI9eVlTgIJeJmzs4CUOCG/5SCz/PoREOlBKScedamd8IWyfWzBdY8V1U371V8HWnRaGXEQcnq8YHfzKjubexzs32VwfMAz165QzxdUPvbKA0QNiOOMb/548NMDKs7IRkZgabVarK6vcePWLZ+KZh1ZGGOSDJdP+OQPfJyqqrh79y6l8WmCS8+5g4MDismES5efYDabsVgs/Otvil2/JnkDxzAJ0NqAUOSFjwTPFyFKtIlTQdp1pP0Wg8k9xCxETSJCl9EOVtlZ2yeNE5JW0uytNaczwySHaR3T7+zSyXbJVlo4M6U8Pub0wSnjVkBv6kiTXaIYajS68YFwpiIyIU5KbwbpJBqDdrVnSPEITD43G1WikXc2nhwfMGlWnLeEQcoIYUuQoJpACukkDn9Q8OCol89aoRBWEoUZwlj6ssd09AZhFVHpBVGUkJdnHB7eRcqAZz/2Er3+Jmf3buGcYzabMBoNyJs43CeffJKdjQ2ixKcHzWYzZqMB89mE+/sPccREofeEU84Rt9r81me/wNt372Is/Lm/8N9TmojNzUvceP03+b17d2hXEdPpjFmZ8ydefIHnX/g+Wr2U4XRBd3UDpQRCCSbDE+IkotXbZHQ0wtU5rUix2e8yLmp2dq5x/eqHmP/2V/jxH/1p9i6/iJSaJz/8ad5564scPryDBl577wHtEMIAsiwmS2KEkAwnU7IoZG/3ElGY8va+QamKaV5xOBix0a9JIsV8MSWJM/qtjFYmyRcVKpDs9Pus9NpMZjmTWcFsPqF2FuXg8PSIh6fH9NttruxuM65PKCantFs9rmSrbHxij/1X3yEzNfF2j65KWYTWm9U7BcZ3863WDYtRIoT1FKMPdiae77WPs2AeB3DA+9UsjccBz2C23rQffLCJ1ppAKh9akmRkScq8rqm1pqhKFkVFbfQ5i8Izch4HHN6fiqW1ASUIlJedF1WFtk12ZbMnh1KxWNYLxoKSuAYigaU8wz3GeHn8nTfDPfosbeO1Jzw7wOIbFn49/9ZF4nEj199P+iGa8Alh3TkreRmp/K2MhEd/RTrPYjbGkJcFiyKnqEq09c60SikCB9ra97235d6rlCIMBaGALBB0W226rQwlnI9hb2qk5f2vqoqiqmi1YqIoIoi9tcX34nicPbb896O5+ugZ8ibXvnnzPvb20t5ELM3xA5IoRqmQRbnwIE4UsSgKYmHOz+GNiNoTNJAEwiLx6ajKeR8ii0GpEBtYvOljgIwClDEo57A6xAYOJyQySD0jXEpv1t28RG0tERFKCmygff3pLKEKUAg6rZjd3T0iFZAmEUVZNqBUjRQCib//Kk5QQpBEqY+4F9J7GiKwLsAJKDXk5YLpdMxq4f+fKIrISx86dDo8pZV43OFrL3+FP/vTP4+uDFZI36hqprCi8bX8Nvfqm5nz345xqL7p8bLfdi38w23U3zGIo5SiKhtJUJIQRsqDItahjY8szpLU+yngO5RlWaKNl58I6Tg98x2wdtYiCAKKomAymfgIZykh4BwomM/nmGqGc96nwb9hTVEscE6zvbFLJ+vw5sP7HB+eEbicWZZSlTFCa3AVw9MHvPnyF/l3r57x5c99ja6CnUTQY84X/+U/YDqMmQzGrMUhKyJHScH9h3eRYcDa1WexMgQbU9WCWiSIJGVjdY/Z2QOKYsLZ+IR2v0evs8a9yZBa16z2+uiiwFqvIz4vRivDPM8ZPjyk0+0jheLw4RErKytYp1mM5xS6RqYZeV1DEHpdYbFgPF/w7u177J8cYJxlns+otF/IsiwjDCIu7uxw5fIOWTskihRhWBHGYSNrC4mShMlsTlkVaO33USklSWOyXBQlQRQShn5CzudzLIIwjqnCBCEESZKytbnKbLZDtx0zmnz3Fj+/YfCY74jwh3/pTY6d8Zz8IAhw1qsFwiQmylpNbOQBQaQo9Zy8KAlDLycSEqI4JMsSpHKU1ZwoaK5VGICrGY3nfP7zX+GLX/wyR/cecHByRv1ND3FMQr/fp7++xp/6iT/Nj/34f0in36OsS3RRYrU38krTFJXEnD08YDod46RPeEqjBGccVBXD4RCra4wUBEHAemcF3Zh/KqUw2lJUJTaK6AaCKAyJw8Qb1SqB1jmjyQm33n2LO/tv89yHrrO2tsXZeMLB8YDx2SlO11QWjFVs7z2BDCPu3Dml11thNs+5cmGNn/jMT3Ln5rv88//7Vzg8OGO9v8Pm6h5VnXP//lscPLiFtZZ2u83a6g7ttEN/fQOEYTqfY4Wl11sjzRSnp8eN51VMkISs9Lve9aYBL5RSqMdkSH+gWPu7MASCANXos/3r0zTJFk3haK0laCRXDvxpBdDLzpl7tPEKp/zB2wpMbRHqsYNQI22Jqghw2EATmhiHY5icstm/grAxqVjB2JKnk5Kv9zMengxZTzrEKwH5fEaaxiACkjhCOL9Ra12zstpB1zWLPKcbeRCg1+1SmwlBHJM5QZLGTCZTVlc2KMuKS2tdRlVO1u7iZpbhySnZ6iZRO2Mwm3DpyhXyW+/yzFMXqY1jPC04PT1jZ2OHo/2HuHzO7sUL3L59n0V6k5c++cMsKkM6mVHJkMHBKa++8gpJEvH93/cxBvce0lnpYh2sbW1SVjlnkwHzCi712ty98zqL4ZCDd97h+PiYAvjExU1+5PlneXB2zG98+avspF1WVtb40z/9Yf6r/+Rn6X3kRa69OmD8wg2i/QPSFe9TVlUlUoEK3KMq1t8QBAohDbKheBvnUIHCIHDLhA4hQH7AhxXnkAi0UBgcdbMeRmGIsQ5cRSQkVrZ8SoxyWOXXSR34g4rBYZxFCEeNo3AaY6ECtAuw0uGWCTPK4ozX2kuhCLzxAtZa+kmE0QXd3efZ3PkUP/OX/gvu3njXFzHG8txHXuBv/fX/joWesL67iZWK0XiBUJLxbIKuSmpbs3Nxl7PDB3S7fUptOD46ZTAYEkeKdpzw7nu3GeczVBzw0qc+zTde/RpVOePC1hqz40Om0ylvfOOIqvYpZdGlS3zj1a/z1LPPUxUlYZSgFdx9uM/ayhqBiqjLyl8/DfOyQEUdFoUjjEqeePIy1d1DzoYz4oXlYvsyLlnw1Rvf4Ohgn5OTfWJTk8SOfjckjWImw4Jnf/gSWUfz4sc+xfr6Oko4pK0p8oALl68zW1knCkLWMoXctBg7o5jfxmDRGHAV0gU4/WhOSeFwrqaymoXTqMDinKZ2NSJSWGO9XEGDqx4Diz+Q4SUTSyDeOdMUwpUH5p3yBzHlASohHbgY5WiSqxQmq9l1TyFPalTsmM2O0aMxi/FDinLK3u5lej/wGcrxQyZH97h77xazyYTRyRmdlTXWV1e49sQVgiBgPB6SlCW6KhmPjhmP5tRlxcPTEd/30e8nDGIqrVnptbj45HMkv/VFjiY1f+W//Gs8vHPA229+jungiKcvXeNgOOTnf/IzfOUrv8ne1nU+8YM/SrpzFblynb2NPtZq0naHxWzOxWef4/Zrr3I8V+STmvngFGcM3bSPCiS39k/4td/9u1xuxdx45Z/w25/7p4xRXOhG9LOMp7d63Dqd8Ls3HvDg7Ixf/PSLLBYBGMn1q9eYzKYMpjNEMKDXDgms5XAwZbOb0UtazIqCovTAVZrGnA0921pJQCjeufMQU9c4oZBxC2tiptMRRa2JlGRzo8d4UfDw9JTV1VXWLl/l/uA2K6XgQvg04fUP8eqrX2c11JyqIdOiorCGWTFmc3MHnWh/5DEO5cAZiXUftF+Yn/hLKZMQogFYLFEj257P55Rl2RxIHWVRNz6eC8qiJklSH91tBSudNrXRJFkbGQYU0ynTec7RyRmLIiev9blEIoqC84b3Mn1pebiz1vqulnVoHEo4Cu2YLBZUWlOXNWVQY0uNjCOENZ4pgwe7tSl97Lt6BJwY8Uj2FSjlz2LNmvuoHvEN21rX/u9L4SUqBF5ZsAQDXOgv35KCKgQK0KJhXaoAqxU4//+JRmLm3LJk8XXQuezYNLWRM2ANoRDMtW3ei2MwmXLn4UMWusICYRhQFDWB9HvyEhByzhGG6hwoS9M2WRSSKkMc+xTfJIpQzvozae0JAWVZMy9KiqLCOEXS7rG7dxn3zuEHNhO/0/HtJPXf/hd9SrAQ4twpdMlQcs6DEFmcsb27Qxr7hr5KAqaLiiiKeHByhLYGGUqCMMY0ipggTgiXztbC26pA428ThLTjDs4JdOlJGUGSMpstkE4Qhgn9He9zq+sSD6hV4ByBlJ7JIz1zi9phtKYsa99Yr2o67R67W9uUeCPxtXADsOCWRBH/HHtPZ28RIq0DXVPVOdJpqjqnrCoya4myFrooqXRJUVdcuLjLYP8eV64/wZ/92Z/mn/2jf0oiA9597z5Pv/AhFnmOtqCiCFv5121+H4Dl8Xtj4ZyJ8we1npfJ3u8ffwxyKoDKWILQgwKLxYLRaIRttwjDkEA6jLGUZdlQBcV5Z6soCyrpo0Hj0EeJF0XhF5PGN2fJsvFMEL/YzWYzwiSm3c588lNZeoOxIECoiunsFOVq4kSiRYmu5tQuRIYBYRDSLhLuv/M2b79+h9/9+ojDm6f0teLjT+3w9F6fwc03OJy0SNMWm+2IlrIYEZEvJhzfucdHnvoIrV4HiQGnOC0iRNhGxhFJp0UYgaYiihIWzeIfKcVsNsXoAqUUa2trlLpmPpmjlCSNWri+QqDI84Ikiji4vw+AqSukVCRhgLTaG80Ky3T/LuPxlNnpmESbpij3dMRxbtDlDBeGHNWCYj6j1+tw8coOKshBepCnKhRBVZ9PqbouSVoZMhBEQUir1UJKSRSGOGeIQ8HWzi5FUVA7bzJblmDtmE6nw4XdPXZ3L2DMfeazBd+eOPbBDyH8IQJr/NePtQ2sgFpbKutASWpbgouZ5zPmi4L1tZR+v08cx2RZgjYVs/GU8fgQqw1xmtDtrzIejLj57l0++9lf55237xIA3ayFjCUECmMsCoWrYDA+5Gh0yMHfu8+0GPHcCx/m6evXkEoQqYDZrKQ0NTjYPzxACcelyzukscLZGlNqnwamIOl2zp8NV5YYF1Loglarg5SqccxzWF0TIYmEAhkgtYFQcu/uLd584xWu7G3Q3tjhbDzlbDBkXhTU1oHW9PvrvPTxH+IX/tP/lqKs+Sf/+O/z1q2vsNjqk4SWCxe3uPTEJbJOxunZkNm8ZmP9AkkcECivGTV2zmI2Zj47pi6HWFkTdjYIYsnx8THjceMzISQLFdDu9Oj1Oz4FB4CgYet5RsGjxfF7E8hxzvrDkZBNR6SRl9gly8ZRi0fR0+eeZ+ZR8tSjz/b9xrhNU/CcMeHe39VaxoSraMh8dUGsWsQnlm6nRVBVfPrF53j5jfcYlZLCCJyZkSUtSm2bFDCfmNPrtJiORui6ZH11hVbo01tCqeitbHN7/z7XtrfBOVbXepyNBqysrlMzJg1DZqdDPnRhkxcuXubO0YD70ymBDDk6PaOVKOr5mHx4ysPhmE9/+tP8xr/5NVZ6PebzOV/62je4dPVJJqM59+7tsygqrl6/hqlynnnmGd69eYvxeMxvfeG3eeFDzzO+fUqnnXBhd4PpdEQaZ2A0v/v53+baxYtgJXvdNfY+ssLhyTEzecrKVp9nLqyQlY4ffuEC2foGH//Jz6CjABf2aa3DtF6wc+UJqvLY71v1GCM1UoUIFfkusnRI5SO5hXAI2XS5hDff9kwcd37/vhvT1Tbrm8AfhpUEKXwaoUFQGogC7X1tAuFZbtJhpDe918J3z511VFJRoXziFFA1ZOBaOGwgfeKZjFh6BdmG5dMJuuTFnI3NK3z5S/v849/724RJhxdfeJqDgwNavS7v7t/k+3/gU/y9f/B/8Uyk6K500XaKtD7JxBgfC/rG22/RCkNOByMckmc/9CGuXr/Ge/fuMjgb8OGXvp/Dk2Pee+89vvr1l+lmvuh8cHjKk7ubdHrXuXb1aUQQUlaGeeHvT1UbTg+PCIOYiclZXV1nPB4RRjFxnKK0QBuFsZ7dNFtURKHCScFzzzzJ1772NdL1dUztvchWNzap6pzjgztMK8WkrDmc1WhdEiddnrARq91VWmlKaEt6rRZRFKM213zX07jzYjsVAcX8lMlwgnULtMlRwQypapSyiIZOGypB7Wq01VTWp3AgNNa3XzBuKX6AunK4D9TA08vtPIDjzp8FQYgU3nxfSoldyq0ECGmwKGKhMGjaYpfkcMKQI1aqDovRPiJ3ZHsXeGL7+6mLI/LDtzk6ekB3a52t9S4bK11GrZTJdI4NLMLWhCqilXaptMEEAhm2sTKnEIIkyXj5jbfY3dojaneozYDw5mv8zpv3ePrjP8WNz/8f3D0pGeYlmWoRMmK6GBK1t9nc+zDXL61x+Uf/HOn6HlGWQFUjZUzhLEmWIloZz//ID5P119GTT7L/uX/I8cP7iCzgnfdOuLa6wX/+M7/I3/6NX6V68CqtTBA5cEaDWZAEoGvLT/+JP8NvfOMb/JMvv82f/PAzHJ3NqIDNTsRKv8VivmA+nzNcLKhFzP64ZlxOSJVla2uDjV7EoqpZXVnxslyLByK6fZxx1IsReTknJyDJUnphxJNXdrC24vh4QKfVY2VzlzNXkIVr3FmzjKb3yKqU60/s8t7+PfpX19GzI7QSpL0Ow+mCzsVVKp03Mi0vQZV66Rf3wY3HjeoBb+C/TKVqPpQMG1a2bAAPsMb7VoRhiNP+zLLe7/lEHSU5agzzJ4uc0WSMBowQnpHSJFH1ej201kwmk8d8SxomLALDssnj5anTxZzxdEpltPfa8hzIBljx9w6rvUzEghPBeZ9BKR9agHXIWvtDvLXnRsnGGOZNc1xb54HUOCEOApywvomGwgrPOrACEAorfF1recQKl1J6pxxhcY3qwBmLCMSjJqtqzHadZx+z/Gw9+6YufVBKWWv2D4+4s/+AWZljpGyS9bxfzjLRxznnY6+XZsbCN/mjNCYSFhVGVLWlCiXKeQ+oJBSEKmQ+yzk9GbHS7dHp9qiNI+usnjfWPrjxh5NJGWPQPMaEfaz+k82pa0mCRVgiFzRpRw2rVfqf6+bv1caQRSG61aIWgiyJ0fWYzfV17j54yHw6p6w0cSv0fkzOEaiQvC5IhMQt5f5G+UCj2pHXcwIVIQJFKAQuSFhtBcgWYI1nfQmHNRpdeWWNNgZpamJXIlzQ7FGWIq8oioJ8lqOUoCxrBieHrKzvEKuUdq+DFCEIv0/oJgG2LiusE3SE5yM5aygmI5SDOg+ZMKd0jsIJRBQjVIRzgkiEHNy9z+WtLRhVuFFO1Oug0pAb+7f51PPPQ1Gj5znKuj/0kvWtWbN/wGz4lrnwx8DEoUE/pfKL2eNItsRimkQlXdUNSuoz5621jcO7w1mviXTOMpnMCALvQL2UIgHnC6hni2ivHxUWY70RV1EUhEmIQzMNZ0QqIIglYRpTVVMqU+EA7XyyhagClHV0VEAngDiUXNlcZ60TIeYJWrWRIiATiijOEIWlqB1jnVNUNW3ZSFNsQBSEGOEoZgVZnCHTkMVsDC4gChM6rRRrjad4Cy/Hqa3fHErtfVniLCVOW0ynU1+0WUldVr74Vx45bwchKnAkgSSNBFoZcqHZaGUUVlNbjZIWXWkyJZnnUFc1tZhRZwE6SSnymrjVKPesYVHXKGPodDpEUYDGL4RhA6J5uqjXqKpIoESAlBqhFFJbL8sKAozxXjvtdpuVXpeTk4TpZPYdTaU/jvH4lPe+b55u6qzAmSXd1P9cCdGYLVpMXVJVFZubG2zv7dDvd4kiSRgqhKipypyqKAma2GFdVYzHY46OjhgOfTc4UTGddg+ZCGzgKPMKCZRVSRpnxPho8FdeeQUj4eLeFlnSQklFnESgJXlpzgtdX2BJMJqiqImk8k7ugUQp7z1hjKG0UFW+6x3GiiD0AEFVVb4gcd5sFEDiODo6YDIZ01/tIhKJGfhiIFCRB2ZnFUmScPXqVV588UWms4LPX7jKV1/+dxTdjKphBK22u3RX2hyMJ4zmU8ra0O/32NzYxbkFVT3j4IFfjKX0SQ6iqlAiAOFYLBZoXRFErcboUCOsQ1e+CAiVwjUUyO/98fjMe0TDfpSy03ztlgXH47zk99Nh/Wf7PpmLXHa0zn9TeLPZbxrSChbxApF5CZ5DIOOQ1SxgvZ1yOh8DHjjUxiCloqwqeu0WcRiSz2e0kpggTfDEWEecRayt9LxPiFTM84pWS9FpZQzGXoayKErWVzaZJo40UszGY1Y6GXeGE2+QqxTz+ZQ8n2OrmrW1Ne7euUeWtZnMZkwXjstPPcubb73FhWdCJqMxGzu73Llzh92dDVQg2b2ww97eHqFU1FozH05otUKqumA4GLD73B7D0ZRup817N28gtebS1hZVnjObTSmAditjUMzpKsGHn7xEuLJGWZb0+32CKELbgkLPmRcRW+tbnjIfxizyGcZalNIIqTzLTzSdRWXPQ8dco2JyTbrTo87ldwPcXgI4NCzFJvWuMSg2QjbSJd9VcsIhhMM16UUG0fgNCCrnGUUeXhS+C7XUjTmfOqSWACUShfR/q4Z+v0uWZXzxt36NJy6+xMlwyEAF/NVf/M/44ld/l7s332Fta41f/uVf5m988m8wn89ZCg+LxYK69qbHFy9fYno6ZGV1naKqOBuOOT4dEEaK9Y0NojRhOp0ync+QEk6OD2mnKc8+9TRJGtLu9HBpB60dc10wnU2bA0hAIH1SXruXMRqcsraxhXOOyXxCbDKcSry8wSxAJeR1k9jhLE9eu8rtWzdoZx2quqa2DXhrakTlC1cVKrQ2KAyBUmAs49Ep/XgdYSqytEPa8j43lW4SOqqcTGW4ZJ16scX+0QBjFEkqkEHdrDjGH2qWxZ9Y+s4Yn+plvb+A9zxaOmvJP2xt+Ec0HnlhnLNwXISUBmuczyJ43CvMSaw0CC2wgcLKgCv1ZURg6NNnNp0Q6YDg6i7d1WcxkyHV9Ih5bJlOxwRBQJz1OXhwn8OTCU8+ec1LRYShKufUOqc0YIqK2XzsO6DWMZlNqWVCGKeMpguqUDItD4lXr9JP+xyWQwKRspbGlOWcbnqRX/jMn2L28FWevHaVn/iP/hzdi08gsGhdkuc1YahwBD75TTmCQHDxQ08yG4yID55C2SntkWA3HdJWljdf/jf84s/8HP/qXweIg7vcPX5AXjv6UcVwDj/+03+ZV278Jk+vxfzbt844Gb9FmkRINJudmOev71IWmlnDIDsZVnTbIYNRzpPbAYPhCUK3SLOEwWhIlsSNj2XN/fsPGRcFa92My9cuMDcFb752gtQzOi3Bc9evs7q5xfGDfTqjEbKdEu1IJtWCPLRsz2YkrRWUtOyILlHP8trRPlW+QAYpgVTUwifZmqX/23cB4H5cfrJkxz/+sfydpZzKp1NZVBBBw7RdynfarYzSGLRx5HnOfD6nKCvP1BEgpLduWKYeOefQWlNV1bfKuRxIlDeLbWrAqq4pypJlE0uce9SYc+2qcA4plirWRkwjOAd4Bc6vBQ6Esbha+/enNbrMG8BYIZwkTj1wvpQ52XNkgHMkYMnwkG4pv30sucg6LBaaM+HjjkfCOh5lPDicbWp052XzS0uNsq6ZzOY+5df4Wqiua7S2WLcEjB4DP3xhDzy6lzUW4zyxQ6gArI9xd1Z42XgQUFQ1dW2oKk1tIAiiD5ih+IcbXhoLRj2qHSUC980IgfNnaiUVsfFnzoK6Uc9IL/2JAsrakipDL4mYGYdOFHGakagFIu0SOYE0jUgvTBGq9NfeWrQzhOc30XrplvWSSxlIhKgRgUbKCJ8zI1kaZRvpa2EnNNr5+kIIBboGUyJd4EMAhKVGU9U1Wji0NQTWz1lRVigTkvVDlAwgDilrSxB4NpZqbmAsAxZ1DkqSRTFoQ0DI3AaYuiZ3hrDbhShGBRG6qiknC9ouoHx4yl6rzbQsIY1ZiJqZKXy6V1mjQoVxf8iI8eVZ849oLvx+4ztk4vhXtbKywsWLF7lw4QL9fo849EZy1vqDfqFztNZoXZ8fROrKnC9gSnlfnDxf0G53CMPQM3kaGt7y9+bzmY8Yc46HDx9Q5ZUHe6qc6XROq5NRW0GhDZs7u2zsbHN/OuT2nQMWz15kLcmY1aCnI2LZ53ImkOsRva02m90AmU9Jgoi1IEfrmnYnppYtjqZTbjwomCcB13PNdreHyefUlaOTdchrg7GWqiyxtiKJW0RhRpIkpFFKVRfk+czT8JU3iLIWRBhirS/+Z+M50+kMjMbZmqwV0Wt3CKTCVjUbcUyrl2HKGad3btCTC3prEWfKcTYuiBPN5kpKIKGVrrP/IGcwmXNvNKUcjBhraPf6xJ0WIoyonUCbGtcUoEop2kmMw5LPFxRFwdHhKXEcs7O9RZYlJEmEMzVKCDQWrRuqvTFUuiAIQvYu7DCfz5nNZsyn+R/p5PxOhwCc8IuLj9W1uIYW65zwzDAJQRyRtlq0e11OzwKKYsHFixf55Cd/kKzd5eTwBIeh1gVJLFhd7aKk9wmKooQyr3jttdf4xiuvUxQFexf3uLJ7hQtXrjGdnfHqm68yX0wxtaasa1rdVayFwsCrL7/Cvft32Fpb4fnnPoRtdeh0erSdYD6vMZeuUJU5g8EZ6ysZSZIxn43IrcDWFesbPcJWzHS2oLYlUZhR5wvGgzP6/T69Xo/53N+HKIqwyuF0SV1rqnLBjZtvslgM2dvd4yzPkWrpPeXjzJVSTKcTbr7zNl/4ypcQ8PaomQAAIABJREFURnL44JBagzGWqqrI5zNcp03aiqjMAq1LZKConWNtfY9a58wXI+RuwGQyoaoKX8xaSV7l6LIG6TMOWm3/fJdVTn7sGXxVVZ374iy7LgJxXix8Tw5hGzxGnIMwtgFvwW++pinm3pcm4N5fVC4/P/LJcd/ycykES77S47+blauMk2MqZmTpHsNJSau/Qnh8wNO767zx4D6r6SaFcoRZl/GioLbapxJMFuztbFPnc5zVrPW69CJLXcxwxZxACp6+fpXPf/nrPLHT4+p2n+21FUCiWitMc81kWjB1x3TimPFsQXdjlTRSnJ0NOJlMmL3xJqcnY6489Sw33t1nMpuyu71FJCz3795lb3udWCXs393n5HRAnMUIUzJdLFjtrTIajSgWJbvb2wy0l9ytrG3QX93k7rsPefXrX+fqlUtkWcJ8NmGkc6r5kNfffp3NbJXtp5/h9mvf4Np2j2ltWE3bWKcR9QJnS956+8sYe0SStbl1/zVAEoUJ3f4KcRwznJzgvdTACoPAEUgQwVIW4BCB73ktTXF8rPIf26z7fYcQAqlkI1Xx4bFKBrgGvHFKYb3lI9qKpqh2ja8KIBVGKPAcVCR+/VRWEMmm4+uU7wILhWxi66WTBM7vL1ZELPSQegib7YxrKyHv3LzNx569xD//f/4On/3sZ7n8xBUGdU5hKvJSI2TEfDqhm6WEKmA4GWHqnIOjI2IVMK9KzgYj4qzAWUE1HwAwn40pFlOUM7hqxqc+/jF0VTI5O+PS1ZfIsjZORlS2ZFbUuDjFNCzgWZGjlOLo7hFbW1vM51N6a+usrqxijUJEGYVZMJppZAw4SNOUxWLGD3/qE7z6ytc42n+IytokkUQFAbGSzOz8vLjMQhDVgIObv4eeDnj6qWdJlaCVhGRRwEorIokCsijCWo0Tfcoqx7kOYfhJtOkxnR+h3Q1qPUQIQRq581AJpEIGCVEQYl2JMSXW1F7WpnwHH+d8XOoHOCG9gkPiqD1VzTov9cKA87GwUiTeJ44AZx3CtQhVC6MF12Y7BJMj0DAqS9wzbVblh0jcBm76gKE9INxdw758j82PXmH4xl3yoabfWSW7kqEEBHFCmc9wQlFXmlobnDFI4xieDjmeDbh9OOTHfuiHaKcSgha/+pV3sKbkF/7kS7x5WHPxL/595OyA1SwjK8fcf+u3KEY3ufr0J/ipv/xX2HxiD1dbbr93xP2HZ4QypqgLFnlJt9NiZyPj6lOXSZOIYGOF6Ed/AXHxOV44+N956sd+lNXM8hu//UV+43/9a3z0+rP81H/8V6mDlM6Fazy8f5vTB3f4F7/0t/jzn7rKtb1tkmjCL33ljJVuzG7bsdbqcPvWTa5e3kYFLa5HAe8e3eO9Qc5qFvHaoSNVNYWeEo/GrPYyZoWPw1a2JskiLu7usqg0R/dGdNspTs8pa8tsuuDWe3e4srvL3hOXSVRIWdaQtXiygGIx5V6noj0+ZLPX41d/5fNc/amPstXp03+pw+2vPiBupeTT3DcUGyDEKMMHjCi+r7ECj6RVS2ZMHMdEUY0xDduw9p6JKtAI4dPphPJeTnEQMhiOGM3m3L9/n9PTM2xjau/BXOHTfBpPNW+YXFLX1jPpmkaNtZYAXz8JZJOcBLOFZjibUBpNZc2j1Mql15VwGONl3P7CCs6Dpa1viDvrMFUNzqEsvsFuDLqq0VXufdyQOClQcUKQJI0rWXO9wIM80EhDfEPAgz+N+ZsKzt9HbWpU00SM4xRlncd/HrMZcM5hjfHeNsJias80t04wGIy4c+8e+4cHVJbGu1MjkQgHaZZi8Z6LtdW4JnhENslftfbmvQGSwXjBztoGSRxhqxNOTweUWiBFgNGW6bxkOJ5TW5DKg3Tfq0Mt6z3bYJ+mYXItGV0eYfPSeFP7+yQf+fjhIE4y5vmcfFKyff0a/Y01ysWsIVz4M/gTTzzBpCyYTqesbngChTTOJ89a33xNMp/i5e+pryeUw4NpVgIG6woCqXwoROhwlQf/HvlNLZ+9AIFCioh5NaUsS+9lIx99ABirEUoSpgmBVDglCFSEjJrwEylRDStMGod2Gm2qxhxfUoYBQgY4pQlkyNbqOltrqwQNQyxOIk6aOrV24AKJ0Y619XVqozEmJEtSbFVivsdsOb8zEKdBTlH+gBg38W7LVAelvK6zLMsGdX4UYbek3TnryNptkpWEzc1NXOPHMByP/ERSwXkyy/b2Nu+88w7j0YiVbh/Zgfl8jhKSNE3J0i5VXXNyNiJQiktXn0bXcPOLX+Cd2wOeu7LB6voOwp4ync7oBwtWrgQk/YDi+F0WRc66FASZj8fNVtb53ZsD3ngwYaADRNSGuIULEkQCoSvptjP6KmE0HVGUFVHDYPCLfUm7nRGE3nQwCUOvwaxK4iii3epydjakqjSLqU/D6PZ7uLLw7tpKcvHCLlmkCIdjXv7alynmI9bTmpUsoq4VbzwccfHp61y50KPNMZRzeipkO0oozQrfeG+fm8MFcSDZ3dnj8rMXCKWiqgtM7SmLVVFgnDemTlsZFsnZ2ZC1NR8BF8dx82Ao4igin89ZLBbMZjOMcXS7feI4JYwD+v0uO7tbPHz4kFMx+qOcm3/wsA4rXLPALRWgj8bSqNh3MgzGBN7zpdSUpqaqa1ytUSLg+vWrOFdz8OA9yrJke3sbqSp07Q/N7TTzmk8DDx8+4O033+G9d29z4cImP/dzP88P/Ykfo9bwa7/+K/zKZ3/NyyqEIG53WOQaYyVrm5eYLiYMT0/55X/+rzg6OOQv/vmf8y0D42hlCYuyhbY1euH1u8IaOq2EMi+pnS8OqsmMyWxBlq5QuJJ8NmVzbR1nNUYbgkCy0usRhj7G1FiFVIbh5IzDg4dEMXQ7GUfjMdrWpO2EbrfLuChAhlRlzu984f/l4HRKFLd5843fY3MjI0iUN3tOUkpjqYyF0BFnAe1eiyTJEIEl7a7hVIR1AYaYejTC1o71zW10VbCYlxRVya3bN7mGJOt0Ec5hdMlsMmUxL0izNkL6zr51FoMjWBYT32PDYTFOI5Ro4hEd2jZdRtkwH6zxiQ7OncerevVV0x9/rDMnDAgh/QZoBSJU551LD/y4RjbnEx7CppNnQoM2HUxQMd8dI2WLeibpXOwTzie8dHGD44FCRiEr7RaRNQgZ0lvNUEVKJGu6Wz3fidM1YeToJClF5WgFgtXY8sy1K4RowrBFaUpGowFGQLr9LJtXArZaEaemx3x0Fx0ppvkUYTSba22ECfjB/+AT/M7XvkC7s8XvvXOTl3/vDS5t7fB9H3mWaX3GnZMZzz33HItiwuJMMjp4SJykDB8eEccp7924gS0L6umIB7MJ92/fwUnv3bV/+JDT0YCLFy9y//4D4oNTBmcHXHz6WUor+Ov/8/9CFEX86LVrFNYyHp2wtrnNZFGzFW2zc6XH2693mOiHbO6sNob6BQu9z0JbCDrNPvZILuVTPCxeWuVBAd+lfFSkf/AR4wIl4/OOZaAC6iCkqB7t0845CukZNcIKpG6MmWXsUz+sN7i0QCgCpAyhKeCXsa5IT7M31kLgv6cQRM4Xl6pTo6pNhA35b/6H/5qPPfNppn/zf+JXf/PXWV1Z55M/8ENcXt/l1uyU5z/yPLfefQfnHFevXmWsZ9y7/x5rvRVOTqeEJiTpRMxmM1579Wvcfe89hBD8yA98nN29PS5sb3DrxpuMz85YiyWD+w9YX1/noy9+jKjVJQxjklZGp9Ol11vhdDH1gQz5An0ywVQF5aTkVJ+ysXfBJ/RIQ6vdQqiw6XLXhMIXhiEVK1mACh0/+zM/yT/9pV/i8P49qvkM5TT5oj6PUhUq9BI14xhPBmzv7WJsTV1VJGFEO1Z0MkGaRsxmC9K0RSAtceQB7iRaR6oPM12MORldoSxPsOaIQD/AMccyIQxaGFkjTY1xghpFbefUriLTCXUEWhqKuv6DBfp/pJPRR9AHQniz6uUB0HIuc7bC4pyX8S1NL0sx49LgEicrBcfTB8wvZcSjgI+FP0CrblFywrS7IL4fsBjeZNGeYt8wpK6FFgMUNf2NPuPhgHwyRjVGyrou/z/m3jzWsuS+7/tU1Vnvuetbe53pZWY4K3eJpEgtlkjKdrQmURgDRuAAsoUEMBAECQIkgJEgRhYgCBIHcAwEMoIEcZzEgixDloRIok1R5IjrcPZhT+/b2+9+z1pL/qhzX/eQEiIZ0IgHmLnd793uvu+cOnWqfr/v9/OlLHKqquLo4Jg7RwdM8oZlafnwSx8kyboUteHiuYu8+c6bfOiDz9HrPuTLX/17TGY5iIZEGoab5/nkj36en/3b/wmDTPLKt97h//jff5ej45uMD27RTfvM5iec2b3A9u45tp/4AA//+/+B/+p/+Xs45eif3SLb+RzCKv7JP/yfOAojPvUjn+TuyvGdhwe8+r/9XX9vGR9+Meh0+as/dJHB1pB5teTZc7v8vX//wxSVxpUOrUuGox6dJKIsllR5yTube6jQcjKrcZHEuYijmSGWjtLm9FJLGMB4UZIGEdPiCEWNMIZFLriw02d3d5fr797kh376IzR5xfGDB2Q7QwabOzy49gaDZ1/iMCzYWQhsppgf77F9/gyv/Nar7PylM3SShI3nN5hPJ0jhHnFReP/dKwLA1AgR0hhHnPRpas14vGI6X/lGW9WQ6yPquiYKO0yK0iuhwxAl/bzZ7SYsc8P9xZx3Hjxkkq84qHJWcYi2FqstwkmCxod+gLdJat2msQm/mfW/98UeTeUBrU7iXIyRFq0ED+c5J7MlgzBC5DUuigCJCiRBFNJojbC2LZB60KsQPu1JWIe0DlVprPEWprry7oa6aZh2JWkYk9oAV1SY8YJEJIgwoM4Ey0BAFKG0QkiJwSGDAOs0BoF0BmMbbJ2j6wWy8goi4xpMvcKaABU5XNssWKtDlQjQ0tLYHFxDomvSRUltYDJe8tX7e9xtHEKvG1QQJb7I4MNiAkIVePyGExgrfeKu0xjtCNHoqmE2PqDa3qBjuxxVEbNyybIuOajGRBsJSzRzbVkWJZNywffnA/35j8g/Dnr7/e+yp+l+UkqvamrXgWsl4el7ncNar9QJhPQKEizOGQIVsbG9xfLkhO+88RZXrj5JfzAAbZktF2AsOzs7lAf7rFYr6rLyaaVR6AOLQs8kKkuPCalbELSUEqPMacPCqhAVyHaut2htaNqaQFPV5KsSIR1Zknq4vQtxTqIbj7rACsIwJMtS4th3Tcoy98Up59XDa1XYugBrjCGWgWfYNTXopuVMWqTw3NHSVFgkO7tn6fZ7pKlXCOb5krwquXb9BnsHD6mtI+4O6WQdzl685NPXaBuxgfuBC8j9s9uppK8or1YrlnmOihQuCpAtyNO0BPq1qgbcI69pW/Veq25wDiHV6WJzLZVbvxcEvV4PawxlUxKpAIEvIHnpsSaKAqRQSKU4e/ZJdG148w//kHdv79NPEgYXO6RZl6ZsiCONDCTFcoFSDaEFl0Z0ezFZv4uOOuzN9tifN8goQijPNkEGBHGEs54hIJ1XeWTdLnEUtA9afJcJg/W8LmTsB1ilNShfEAkCL2nvZD3KKidNU4wwBKGvmIahIsBxfO8WejVnq99lt2+o5zMW8wbnDL/wC7/A01d3+c4f/F+sjvdQy4JR0sepkN3NhImwxIMemzsjhqNNlJQsZ1NsC+AMQv8ahiF1XWORvpjUduq8XDRCIjDGeyirqjot/jSt/Ui1HuEgCKjrGmff/9EtT7f1f0zMuPN9iTUQ1ytzLI3R2NqgtUE3liCURFHE/sEe4/GYjY0hgUfJEBCgWkp6kTfQGObzJZPplFobLly4wIc/8hLPvvgcX//aK5wcjSnqhizzaV7ChRhr2drc5WM//KPcvPsut268w969h7w9eIfj42O2Ns4QpSnShoRxQpSlxHWCDA0IQ934LlAYhlR1TV4saYyj34so8pIiz31E92DAKi+ppWk7E17Cbp0GC2W+Yjab0e1FZFlGt9dh1WhC5ROOBOCsJQkj8krzzndfQ8qIWleEaUYgrN/AWUlVVNSNQQYRaafDYNBDqADhLLV2VBaME5SlJQpi+tsjsqSHizsMRysWiwV14+cRrTUqChFBhHGeL+LTbSTW0Up59enc+RcRTfqnOf5Vwcvfl9ogxGlBZ/36x77Prcf/o/hSWi+0kYag4yiPGyIJ+apiMBiQW5BagbVsDYc0piaLIjppxrDXYzGfehFJKGmakiRW3hJaF0ynFdujAXmxZP9kxvbWCGsMs7zkzrtv8vT5DlbE3Nzbw6oQU2t0WbNczNFGk3W6HBydkGUZm5ubIG4SxSnT+ZKHh0dEoaC7e4HDvYd+wRbFxFFKEgbcv/8QpRSz8RH3cQidE0URy6XmZDLj9ddf597BIc45Dg7+KaPRiMViQZYoZPAKtXGUjU8cMlFCXmh6owCjawadhLfe+BpXzn8CPl3x5a/9j4QyodHay+iFxVpNdJo4RVuYcUgCb1lBIIT0eGv56KH/p1mk/XkcjnVyCTjpbSq+sOQX0xLaTXT7PVQrJ38s+LIdzo+rwaSUHtYMWCmQ7ZrAONHa/sRpfKZpLKPRiNWqIslC7jy8xStvvY6MQh6e7POZz3yGt956l+zMFs8//yyr1cIrY5Zz9k5O2Nrc4fhgn5s3r/PkpfP80bf+iDdee53JyQm/+Iu/yO72DmkoieOY6WTMYj6jqSvS4ZDdMzsMNkZs7G4TZiO/NmmtZHGYcHbQx1rNcjpjOZvTrFb0+hlCSqqmpCvWGywv1w/kI0C5cI44jhDOQ603Nzc5u7vNd775LSLg4ME9bFOQpNFp53xtKdJak+c5i8UCWkPbeiG67ogqpVqm1iPlXpp2cNIxW80IggFNvUIXgJA+KcSAM5Y4iGjaaxiIoFUYKG+zkNKn1L3PiWnSSa94k77bDr7YLVp2kqVNs8GBUzRK021STrY1k+keQ6Exq5iXNj5BX2zyQH+TmSro7jdIa8jdkkG+i9h0PLh7g53tkS+aWY1UEIcR1tQ0jWdv6LoiX66Y5jnLVUGtA86dvcBo9zw2CFCVpi4sV596DqtL4lggzYRBKinKms2NM7zwoU9y9UM/SrcDd97d49f+79/iS1/8xxzs3yQOJBujXaqq4tq1bxGqmKsf+CjnhgPe/PrrPPuJ53xTIpAMX/g4H/nIpzl6cIPF0SFb6ZL9ZsnVM33+6MYhT5/rsdUJEArunCxYlnOyOOSjzz/vr+VqSTfLiDvbqDBgd3uH8fE+x80xz5wf8XA+JkliHIbK1sgSop63SpRljkoizm4NmC4LFvOSRSXYyiCMHbZocA/2efbqFeQqZHR5i+WkoAl90irbfY5efY3V81uciAlXzZACx5VzQ/b254RhiHGCWEgm45ytMylN7Tw7xXnyy/uLNnaEQYwx/p4ry5LlcsXBwQmLVYkQiqIqWZRHGO3IMoO2Xnm5WOUoWdPt9jFtil5Z1q3dSj8Wx+2LDrJ9HhvWRRzTKvIf2WL+5GeDf4/WtW8CNzWNcacqhvWcsp4zRPvvSutO2TPOWA85Ng7aP2OMx1qs030jGRAFAbFTGB1SFQV5voRQocIeUZRgjFfsBCrEtpZbsKjWJewes4g7Z9EtCmO9z/N2oHbe9G/EOnOqIMF5q71rz9F4OvFhKto3Z0VrW167B9ax7Otzt56TtdY0ja+OhRKMdKf2tbJFCzSNTyy2WuO032PWde3tNFX1Po9FTs/b/9/hXKu8URKsV6Vz+mx4r1u77cvg5DrKXbQuhICyrojihK2tLY5WS8bTCYPNDXq9HlJK5rM53Y0hnW6GsD7BK44ClAhompJYiVNFlW73+apNkJSn16ptWmrtU2RwCGuRIkTb1Sl+JVyvbbEI658H0gkkQVvMD+gkGUHgg5SKYuWdIFqjnSawFodEErFOhRWBILCOoqlwwhdvnPW4CIvAiABtanq93mkjK2ihyI01TOdT6qbFS0SKsxcuoFRIWdaEUdbeVyDlnxDl/hd0/JnBxs45iqJiPJuS5zmDgeclNJWHEufLFT6hxfr0qjA4pbHLdhERtNI33TSk3Zg08pHPnhzuCwRhGJIkXbpZRpHnzGYTmrYDGMUBVV6gbUmja7qdiFE3ob/Z45nnnqP83M/zR//8NzgYv0Xylz7EE5vbDNMRyWBEmc+ZVTO6nYx+0mGgugw2OhSEfPmdfV7+7oyDpWN0/iJEAbYsaMolMg7IOj2q5Yq8nEAYEXZSGtP4bsNwSJNrZqult045R1X5zfd6oMdpwvb2NlXVMBtPiZWkEwWsasnO1ogsiZlMj7j94D7V26/x81/4Ah//9Mf55u//OjfeeJuT2ZxpbunvXubMlcsM39pBlAsWBzfpdiCJM55+ImMaG0wq6W31OHfuPNZaimJBtbQ0VemlaUKwWCxYLpeoIAQZkGU9vIUgbCfGmv5wQBynOGOI4/h04Pob10cvrlYrprMx77c09vEx+fgE8p6vS3HqdVVK4FzoIyJNRrffpTGWfq9Ht5dx7/5tJpMTev2YvcP7YB397oAwSElUh9pAVTYsFwVxFHHlyiU++tEPc/bCWSazGb/+W7/JH3zzq8jI2wwaq6hyzTNXP8rP/fwv8bM//2/y8le+yJdf/hf803/yq7z+2tt88Ytf5Au/9NcII4FoBBs7ZwlWIdVqn0CsE4v8AzlQMbUxyCDG2ZK7D+8itGRzcxMpIRD+3ohVRhj6ayUDBabm+OSIm3du+IQrl1LXNVmW0l019HqSNO7gBj1ip6nMCik1pRYYt6Q/kPT7EESurZzHTE5W3L1/wNmdczz51BW6WyN0bbh35xZ5UeGcYLkq0MIx3NpgY3OH2XxFqKDXG1AUFbPZjKZp6PS6CAJ03dCNU9IgQTmJFY9scAiJJ+8D4gesiOO+H3uyXhCIFgQnpVcn+K3/oy6kajfLAv8QEwhU0EpMpU90ebStXpt28J5jPNtpPb+gLRGK0jlW0RS36eiMdxk/mHHxzAdAHnBS3aTrOgRxB4chkIpnn7rEydEx1jaMeim9Xo9bt26RZopIBcyLhp2NEd0QpuMx1sKkEYTTKTujLp3NHbbS+4z6ITNSXtm/g9aSJ0fnGG10eXk8JU43cOmQL331mzz9zC63b13nhz/zYwgk333zTW7cfYjWOc80NeM9yaA/YmN7h4P5nOFwyLvXrqGU3xTcv/k22lXMpitOxjMsAcvlikXtlTDD0S6L5RKpUrqdAUEccbRccTKfcG/8gL9/dMQXPvnDvPDMig8OBvSyiOc/9tP8yn/2y3zixSs8ffbHmYu7BEZQFEuUDInjBNVKmdVjixkPMpanAyAIWr+4eFRce7+f804IjAqwSrVS9gDr/L0bEiCNIkBhbdB+Ts+2UUJQWW+BFsrhlC/MSOel2X5MCozw3AUpWxm/FI+iq53DWF8+Eo3j7oObBFFIUVnObQv+vV/+W/yd/+I/J0lifvO3/xmf/exn+dCLH+HBndtcunSJMAxYzaYsJzmJjLh+7S2CUPC1b/0h16/dYnd3l1/5m3+T6XjSpr1YjqYnPLhzk+1ewtXzO3zg6jl2dy4gwoSgN0AEGSJQZHHSXkC/KMVaOknWWlBXLGcTb7UJArrdlDRLUTIiryoPQG0mxGkGTUFEgBCOQDouXzjD3/h3/i2+9Lu/ycGD++zsDChWjjIv2o2Ul3sHQURV5hwf7oGxfODJC0xOQi5d3EI4izOaXpa2RevgNNI4CAKqYolA0O9tM55Y6rpDXpdAQWo1KRlChIhQYoShoUEqQS0bAifQUqKMIewY1PtYxHHOeZS2M9461X5dSLBGnYI6XRuBa5HIsmGyalCjhrRxmCdHfCT9CaJowLvzL/KmvckT4QWeCp7AuimqiBivbtIZ9hnuDlmZGqVi7KokFDEkcPjgkKOTE6pac+3ufUptmK40eSOorOUnnz+D6g2JZIdwKDg8PERQce3tezQKnrq4yY29PfKiorGGlz79OT76uc9QrSr+4//oP+DV177JrYc3aWq4dH6XBw9vgxRMljmVNtw5vMfuaMT+3i3+w//0v+bDn/kwjYDuZo8XfuaXePBHv8P1177Mixcvc/nckxwc7vGFT8W88WBCJ1WcTEounRsSuIadwQBdlUxOjukkGVmnQ39jRNb3jbg46/LwnWsczlc8OTK8eyjQxicfLa1EzwVhULCZJRgEq9WcWAX0R4InVYqMFUWliZMuShrmiwUXNbibKy49c4WjWw+omjkXL7zAm/cf0ry8j/7oRa65KRc2NxlUMR99RjELYVXnmIGEA8Nefcz2mQFOaL8xFX/Mg/PPdSwCCJxQhEHIu2+/S1GUNBriJMUaSEkwtoNRjiTpECc9jHa8+tpbVFpzudtnPj2hLGoOjg7Jy6otvsaIsgG7fg5IIhm0tirX4iW8cmHdzP7emHE4Rd0A/q/aPzhi7+iYrTSj2NzCaH2a0uuMbXmXrX3bt8P99xqD1QbbqnGcc5RlSVlVWAFxmrCZpigVksoAF4ScrJbcuf9um1gUsXn2DFGSEKY7RFFEKBVYDVjqcgVNgdCVZ0qWJbayaOeRGkIIdF2jYgmyTQtaN+t1gxOWwDp0bajLhqqoGM8Lvv7tV5jPlqhQQfVIfbI+h9baR4VuIVp7nqHMNdZUhGGACBUyDJFBhAxChFK4RlOVNU2zBpx36UQx9arAWsdqNj89dz+Ih1do2xZULE8jqpV4VDzxDQbPopFC+Ahv55lItdGEzjLPVygHZy4/ga4bCtOgZ1NeeOEFlrM5R7MJ0XLB8WRM1MkYDYZI4d01VleMRqPTMXuqgmma1rbUrmOVQAqN0wKsRlvrkRzaUVUNSRS1TF1Fox0y9GKBME5BSYqFL9J1u12kglWRY+qKareh7mq/JmmDFBrjC6iy8ZYubSx1WeGkBumo6grXaJZlQdHUVA4uXr5CmqZorVkVMw6PTzieTrE4OKmxAAAgAElEQVSrkmVeEaQ9nrj8LC6OMIR0si7OCUxb+HsPw+0H4PhXKuI451q6dN0qT8xj1Tkwxk9YQRCQttHV64G2rsQBSKVaD9+jhBbRym4rWyFE660MQra2tlgtZu3fm5AvC6wNfPW3qCjzOU2/SxQlXH3uA9x/+wqz/Tu88sZd0o8+yUa3Q2ewiUwilFGkcUSaJIQl1EazqiruPZwwrwOCThdc6NU1psLWJSJIMHioWCB9BKtlHcMqkMISBJDGabtSscyWM+I4Jo71Kdlet9GgQimcbHziUaCQKiYIQ8ZHEybjE87GIXEUkC8WVHlBlHYIUy8J+2e/8/u8eX2b8PgQmRc4K4iAKISNbgqHJ+TVjKYpCQJvTwuiGBnUUJUY4xOq1tApDzorqUPvdVytVgRB4KPR28mi00k8HMw5wjj2aqhQ0VQldVm8x2f8fhzr2+fxauj3PhCVo41u9GCzSHmLgFIKFfuNWagCut3u6YMhigOqKmcyOSZfFiRRlyhM2ehvMexvkSQJ3bSLEIJ+P6Pb7XD//l3qu0fcuXeb+XKOCgyWAGsNiJhub4uNzfPEUZedM09y5uxFCLwK6u7d++T5iiRMCFRCmiU0QY+ok2LLGunwkkIpaKxCKEOgQmRjcE2JUIJQBTR16WWOUQpCIYTCWIt23tI4nZ6wt3e/hbg6lrMpKksRUUAQNHSzPtIazGJBXtRIGYDSBELQ74Yo5Ugib6fSjWW5KljMVzz37BY7O2e84qypKZsa5wRN01DUFZ0sJEhCqqrAGImw/vynaUqvN/D3oAwxDpKkQ5L4a+GEQ1rae25dDGkhdj8Yc+fpIXgE+ls33k/HJPjuk/WR6adFnPaHCOQjlc2pnart5D16dadfP4UIPsbEOZWoC4tEEcgQrVfkrBjtJqzulpTaUjeWLEsYhH2KxmKdZmdjSD8N0N3EbxqlJFCOMACMJcsyKltQ6YZYCrS1DIdDtFiRryY83F8RZDVXdrc4mJywbwp6G9vMZwVVVSCyLkEUkvV79Do9EuEtPWUx59LVKxSLnKO9PfKZJQwT7t29Rb8/pFitCELJbDInn41pyiUVkrrxbICTyZgk7rCxsUEQJVSVxhnfcet0OpQ9D2ofZRFFozk36pKvJnRUxGRZc/vwBG01566eweiCraenfOUPv05+cMDnPvtZet0FzhmkMpRljnWGgGTtkPMFDNaMonZOlz5Fg+8r4Lzfi0PfdbLCg3WN8JbESCoCGyJRhIQ4G/muXWupcsIDWIUQp6Dj03FsBVKAkgLt7OlPZJ1r4259N0y3FU3nHGkSI2VN0g2oixKrGn78U5/mH/3qP+S3f/u3ee3NN3jxmWco8yXXT444u7vN1sZ5nHOMhj1e/sqXyLoBy+WCe/dv82Of+STdbpcH926zvbXFcjlnVSwo84JOGrM1OsNwMGBjZ4M46xKECWmvhyPxF00ItANdVXTSxK87gpbngGS5mpFEEUGc+I0WgihQlFXjO89twVRJn7IhccgowhnNqNdle3PA4YPbrPIFxWqFagtazhqUCnHKS3StaWh0Tb5csIpUWwxU77HdPZ5O55yjE0ccHE9ZlCVRmKF1D1mGCAKfOOMsURAiGuUtB0KSiKZVLTZYBIGUEFje18Wn8Gsm164BHwE5g9NEqlOPvrM44RVzMg5RNqNazHj+iY8RmA4x8K6+xdX4GS6LJ1glU/TCkh+P6V0cEvTPkV97nShJUVaig4B8uaRcFUymC45nOXmeU5QNtVDIKGW1KNgeJKAEUavYqvMpT1x4kvsPDM6G7M/20MYhXEAWG8xqzrOf/gmM8Wk/7177DnkxJ0s7LJqaqqmptYfXauvT7NIk9Rv/w7t866tf5oVPvOC76kLQOfsE2fmn6d54g6figEle8rt/8BW2BxFPbGcUWhMojdMrzl14CuEqmsZb9HtZys75i0gZkCYhRVnTHY64cOkye+Mls9oh1QrnFImSUHuoaF4Lsrghso5O7K2XeaEpxAKZW4JAMl8UOBo+/uIL3Lp3n+eeepZYpsRP7bC8d8KyWXHx8hXeff1VCmfppjGm0GjjUIFmahtUALKR9LcSXJSw9/CIMxeGrNUm7v2cGoWgMb7gIlVIWVaMJzO2tnYYjDaoKx++kvVDmsZgEdTaK5lVFIIxFFXFZO7ZHWtbSeAU6Np7pKC1/Pj/1DqVDU6LOPDo/l4DfR8v3kDLdpRQ1hWzxZJlUVJbi2zvI+nWP5I4VeLgHkGUsc6Przb9iTaRyn8+RRCFRDI8ZQ0KpegMMooqp6lqxsdHNEXOcDhkeCbBmQQZBdgWCBK4BmsNjW7IF0umJ2PQgrSb+TlSeLapCv2KR6znAQPSGG+R1zWm9s6FVV1zNJuwd3To07Ladc5aadM0zXvO21r55M+FQLdpXcYYdCBa61dImmUMhkOyumHVVNhiQZiHvtDkIFQKodq/7y+ghvMnpVO9R31t/X4zlNLP4dDChx/9WR8u5Ncb6/PWls1wrn3+NI0HPusGlQ2I4pi8WNA0DcZZ4k6KPjny49RYVoslaZzQD+LTz1JV1anLZr2vlwhMozGtQiV0tNY0i9U+Er40lqJYESrPnbI8tt8/fW3XGmv1rxR0Oz2Wi5xiOaPSvjnsbaYVoejgjMZYD8e3lfb/ntUYZzC68favPEdrR6MNi8YzbZQKfTy5tdy5dZMH+3tc2jrDhz/+MW7cuMF0PkNlPbr9jAavZPJKd/uD5qb6sxVxBNBNO8RxjEKRFzVNbQkj7/vupBGkXWbTsVdy1A261ijpE5DWcuE8L04rbXnp48aKqkFKSRqHxL0eTVkxX+RoY0mzLv1oyFhIFvnKJ0n0em1lr6Q2FSeTBU4do2TImc1tfuavf4F7N27wna+8zIMvvcmom/DS0+fY6Gf0sowg9L7sWTnj2/cK7k0qvnZjTq5SVGRZ5kcMtobESqCsL0AbETDYHOGkotKaxlmUkDgUy6LGaUnUyQilj3LN85xO3KHbHSJVQNXUWF16xYQC0QnQKkCQ0JcB5AX62n3E+JDeluLtf/F73Pzi7zIaVPSkJAsb4lTyv/4//5gzseSXPnmGTbWkpxuolnSjPtFgxHJ2ncNKE4WKThp5xVTWIQ0l8xAmE28lGgw3GG3Fp8lhx+OJB4Y1Ff2+TxVZLpeUdUGYxKTtgrep3amvtmpqnJD0ugPy5fjPaZh+/+GV/6I1BkgQ8nTDTCshF5GHoUkpfRS81SAsgXQkQcTGYJfz559me2tIuSxYLibUVU6+cuw9fMj+w3vk05Ikibh47gP89Of/Dc5duMy1797haH/MpSsDeoM+337tW4xXJcti6rskTUocpjgniJOYo/1b/N4//zWm+/epXMHJ4QN2tjbJVxPeeOMN3n79TT7y0kc4u9Mhdw1bSYfw3BPs370NrqGXeXL++GTqae4KwgiyboizivlySjboENaKNBRgK6o6wgjvp1dCcPfmbd556xqrhaXXj3n4YMKlFzY4txNxfHSd/nZElI6odc64zqmNJUs7ZN2Ec09kbIwG9LMh25tnmBye8N03rkEj+ZGPfopnr36A8XiPsqgRskGbFfOFnwPCIEPYgKYWJGlKFEs6UfQoIU3XHB8fouKEy1evkPVTVOQLqD6loWWQOOsTmX4AmyVCCJ86A+AexZKuOTawVi3Y75Nhho9Zb07TKlDINvVBSg/pXr9n/eeCtaVMrK09ICOBWglSUkpytKt42LvN4NlNrr99Dzu3XLp0lcg47h8dcP7CBXZHfVaHD0kDv7haljnFUrM5GmDLEuFgvlpSV7BxdpO7Jwueyfr03YLR5cvsH4+Jbc7BIuaNE8dBsaLJc6q8ZJ4ZwrziyTM7OGFJRMPlSxcoymM+/NKLjHod6nzBs89cYTHN2Nka8vDeHW970JrxeMxykZPGARvDIXlZUjVLZKDIOgM6nQ619kqufm9AP/QF6OFwCMATTzzB8fiEm7fvkleag31DkIQkSL56/R3OnHTZ3ZB89tOfQMuSv/7X/ku++ju/w5e/9A6lnrG90+fp5y8wGOQYW2O1T+MypgK7vl6KR8IGh2qLJo94OOJ9Lzo68IlS67HoBNJJlEoJbISykkTEKBd68GULx3XOQRj5ZC3nTqXZtQJnHEjrY6tbW5nBYKVAO3fqyzfOYqzfmNw7vsNoa5Plcoyshrz19ut87Of+Mt1ll1/5W7/Mb/zGr/PVr/xLjo9m/NzP/RzffesNHt67w6uvvsr9o3s899xzFGVAr9fjMz/2KZJAgiuomxmvvv4u3bRDv5OyPeyxe/ZphqNN4jgl6qTEbadOVxoVW4TzNi8lBEmWYo1ECoVThrgXEWZdwjhqN0UBIgiRIiCQkCQR1IZuJ0E4w6ifkUhLqCQWQ2VqoiDiv/tv/xv+yuc/TyAkadSnKsdtBKyP+FZC0Gif1pelCXWTg/PrpV5vlygMccYQhgqkOFUsW2vZ3MwwLuONl7/LaPsMWTYkU09i7RQz1+ilD08Yqgyn/KYGCaWtKc0CJcA446ej93E8+k5t6ZN3BBjdbjqF8YkkwoBdx48HOKdQUhHEfVxVsdW9wPjoOnZQ8o35OwgSnpbnWVYTdF1S1wfoZyTx6Hmqwz1cEhMWkqZYMhtPePfWTco8Z7FasTc+oq5hVkJpNatc041D9sclH/yhTxKEEcIY6jJnOX1I2DSMD68jKs3R/iGBtgx7Qz71+X+XKJXUK5+2RxByfHxMaXzD5cz2OW7cvIYCPv7SS3zj9e8wnq/IIsUgCek1BbO9e5hyydb5Z0g3ejzxqZ9kdv1ltjausppN+buXn+Pu9Ve49mBMfzCg0+uCrkmD9tnianqJod8Z0ghFJwlYLBZESQxG8dSVp9jZ3uXr336FN2+/yUYG2koi5TeEs9JbGdLIEYWGfif2KbFRyCJfoVDsPrXFdJHz1dducmmzx3eb7/Cp4WfZsjXdl57i4avvoJ65xO5Tz5Llkof9imkzo9fbJqkM+t4YeyFDSFCjgPqgoaeGGFMjZYhAva8qRecEhhDhJPWqpDfaYLYsqRrjWZDJOqnTW6bmq5yj8YxV0SDiCKcNN+/fZZn7pkapNRavgGnKirrUBIEgTmMP6tU+5vtxK9D3Pv9PU3tbvtga9guAksxWBdfv3iULIq6cvcAgFmRZ5tUNzmGVt7MCPla6PZ+ysUjncNpQrXIao2msQUYhQRgQxjEBKQ6BlgKnINrcYrffR5cl4d37zPYO2T8es5rM6Pb7jLa2EYHHX2itOTnYZzYZs3/vLvliSa874NJwRJr6ZnAgBKYqkS0IGudwjYamQRiNrUrqsmQynfHWu7d55e1rvHX7NsYClT6F+a4VH7ZNsVrjOnyBw4dvS+kjyLXWFM6CsczznKjb58yFi2z2N9BBwMHRPrP5HGEsQju6nQ7LoiRrk7l+EI7vdRM4/0R6pLjx33lPMd4rcHwjxtvn/NpTOhAqxGmDUCFnzj/JajnlZDEj60RY6UUXb7zhQ1qa02RUn9g42T+ms71NneeIOERrTRzHvpDTfqYoik4/i9WGIAEViNNCZ1mWFHlFv9tlOBhQNZr1Qt4YjdDaR4nXtW9oiICmyamLhk7W56WXzhIqycnhhPlizNkLuwThmgn5WKGvakD72HrrGoyr0c5Q6obSCApj0E4RRwlNU6FLzerkhIOjQ5qmIRuN+Pkv/Nv8g7//P6OyHp/68Z8g6vQoi8afW+9n/IFrIv+ZiziBE6AbKt3ghCJKYqIoZDYdo4UHH6ZpihKSPM+ZTCanE1eSJGxubLF/fEjVNKeqCG05VaogJcpYVBs7vlwuKYsVDx8cgG2I45QgSUmShMV8zmq1IMk6oDqcHC0JlMJq2B4NOPvSh9i4eJbrr71GtZrz2uERza0DetLSkb7TX84rvlGlLJ3CpkPQBS6oefLJHbZ2z5B2U5yIvEqiOyTodH1FuWlI4JTf44SirjWrskE3M0xdsrm57avRKkYEMaJx9JKOV70UC4hjcmOotKOnNDJf0l/UZFqg6xXzcUVHwhNxBzEKKPqGRBnOhyFPbW+yEQSEZYMwFUmySSceMF5a5lOD63TY3NwkSwN06fyCM47o9frEad8XzooVSB8lXhQFURQQRR4aJiUsFjOvmogTGlNTNRXOSupaE0YJ1jpfBXUtqf59Ht0+cvFRRf70663fUUjlvZlAEAXYujplBIQqYnfrPCcbtwlCxeT4gHq1ABrqSjIadOhGF8nHE6Io4dKVi1y6epVeb5vZtGAxz1tCvqE2miBs6GYxSZQQiIRQRCAdTbXk+OAaBw/e5dr1V9g9t40MYWuzxwk5q8WCl19+mSQMObMzQDclQjik8BZD4WKiUNOUBVFisGWNdQpba7T23SJrBEaXlKX3IXeS2KuBbIPAUi5K8smS0MbsPRxzdKyQkWTz3Ba75zb5wNNneeOt26g44kx6EddJsNrQT/sEiaDf0+xunWdjY4g08K0/+jY3r9/gRz72SX7oIx+j10k52N/HYhDSMl+MMY0myzJC2SN0EicV2SAljRMODw+oiwVNXbKaLwjSmGGvy90HD1hVDc+ulgyGw9ZD/dj1bgzOg2B+wA6fePC4IiyU7/2QvlPW8PgP5Is0wXt+D4B1xGlIWVh6Xd8xfK+HXniZWSuTFi2PRNuEIGyw1hGYDIklEqDPzNjY6HHvXy5Rs5R7xUM2N7YRtWZ8fEKWdmiEYntb0ZsvsNbx4MEJG7tbWN1wZtgj6SXMqoqk22d/ljPo9Mgi6EcNF68+xTt7cwrTQKfHUFZs9HsEYYx2frPbS/0CoHElWadHryNIVsecSw22G3GExOXH7AwGrMoCFUSIMOLChfPUyznaSZwQ1EaQdPp0R47AKXRVkHVjsi5cu33E0dERcbxPXdcMbtxhf3zE9mCDjnX8wud/BCcNv/p//gFxAkKmTPfnLPfvMtw6yz/6B3+HF176NMdzx5d+/xpJGHH+/IBPfuo8O7sRO5cL311VHvAnpaRuIoLQg45VYHGywcnGg6yFbBcb72/vxjpJQYwMQLWFQ2MlyoTELiJS3lZlrPbgbKFoDGjnMNLiHIRGtLlUltApn8DV3pBS+EWYxm+M5NqR5RzW1tROY62m39tmNdVI0eH5F19iNs9558a3+ZFP/Gt04oS/cXHEc197ibgoqHVDozV1nfPpT71IFf8wnU7E0eF9VqsZt2/fpKMMUgR0sxFPXb5CFCUknYxeb8BwsEEYJkRRRDdJMUJ4FaaSqFbeL4OwBaU7wlYBo7VnDARBiMv6CCFY5SXOGozRWAImyxIjFIPRJqvZlHKxYuvMNs76NUyVl2hh2RqN+KnPf46vf/0baOtQTdimRz1WgJWqVeU1uHqJdH2kramWOSHSBwYIQW0Mqi0CN8YwX40Jk5jnnn6GN95+l2ms2IpAKkGWxixWOVYFxE4iA0WNYF41CNEglcXYGkND7SzWvX/j0bVASWQEDs8hFJ6H43UAIcZojPUbem1Coighbwo2dEq/2+X+yQPq0HApOM9G7yrHyxNOgjnZG/cZPxtyafgB8qMDdGTZ6F3k5PgGy2XBZOkt6yWWw2UJ0Tk0jjRRdKRBTnMWVcH2MOXpFz/K9OgGYTwkGp4hISLelgxr6HUHmN4NrjzzMbrb53n2J34GWzUIJdASdsKQjU/+JA0Bk/Ex129+24PBleS777zJR5//CJ3RgPGtu/zUj32OH/3Xf5bANSRZj3x5guz4qO8rP/u3OXr519l85hl2qxVXXnyRDz+8zze+/v+ymN1mMpmw0fNruNHmBWIBaSdEyZCiCVDpDsfjB34NkMYcTfbJ4ognz6bM5jVRAIG01NrSCMmystSNIAk0RdmQRCt6nZRuHHq23aLk6vlzDGKHESk37h1S/N6v8cM//pc52+yyd/mYWhhcPyZIO3ywjnkteYeN1YITUWJnBnm+lZhYS7QtkZGk/m7G8BlJVRW8n2tG4+B4UWFt6aG4IiTpDzjYP6K2N085ap20izGOee5ZbSeTKbM8p6waDicnnoQWBKg4weSFj6muanpZesqZbBpfhJAqPN3/eCW+twRVVXX6uYTwBQhEcNqjUgqscNQa7h+d0M/6HKwKVOw5Y+BVylIGHnqr2hSfRmNxxMYXS+uq5cFUtWfdtDYkYwyNiE95aVb6GOgk6yKDiq2LCb1kxOJ4wsH9axxLwUHW8WoN4xO7Dg8PWS48EHdztMH58xdJkgQlBM40VLlX3WpbtZwvcHWDM1CXJeVyRdHUPDyc8I3vXuMbb73JJG8I44jG+GfP2inw+PH4OsurlWmTj3yultGGMAx5eHxMoTWNCpgVFVGa0etvMBxOvEXYQRyELFxBVZfva0HxT3us+UYov/4xziGxWNVamZxnIql1AQdwQrZkuvcmus4WS2ydIzA4pXh4eIKtZiT4xGlrDCqOODg5Ye/WDbbTlOliypNnz5ImHW/RbWPc/TV5xF9aNyAtgkVV4poS7Sx1m+4WxBFWrNlF3rKdphlaNQSJoG5K6tqn01oBjXE+oW1RYo3kA08/gzWSuqo8v0ilWO1V/8Z4dWtd5t5CWDfk1Yy8KWhqx3y2ZLqqOSwrNs6epTsakec5um44PjxiOp2iopCf+vznePrZp5nnBZ/76Z/mR3/yc8xmc6LegKbSxCrCWP3HXqf3XDMByj5SFP15K1//zHaqqi6wNQRJ/BgVW5xWmaMoIIpjHxvX3oDrBe9aAhfH8aMo4RaMm2ZdnPMwpbqVKXZbcHBTK0Rg0LUjkJI0S9sYNV8ECqWiEycUbnUaPzeez0njiGFvyAsf/xhNseTO9TcYH56wXIyZr3KKqqGYOZJhRL/bRQQhi+WSIIq58KT3zQVCektXJ0VFAVh9WvFNTkHF7YWKAkxdodsqaZJEFEULVmqqU7+sEIokSZBSUjQrpINqWbOaLqiBMElQSiMC7xmvHHSSlK6N6AQ5SW9Iv5dQrJbgDJEQZNubxKMBx3ceYJyl30lbO5RAhYG/wdubzVujNGVdtA+cxkfHJYkHTttHVe9Op+N/ttr7Ik3jveunclCtqauKus5/EEUSj0n1zHsnfyFIOzFhEiKlodI1URShVExtG6+iSTM62wlJkrF75kmisIMTIUGSgIG6anDOUpdLVBiRBTGdMCKJFNY2fu0ibFtMMizyGdtiRBxFiMAh2oSB/eND9o73KZoCLRVGQqWXONkQhdIrjoxXqdWNwTTuNBFOCOWVKtbhSodJNDoISZ3wXArncNrb9tIopJsmLIslh4cHPLw/wFGzu3uB460FRdEQqRpT9zHGkIQdwlAwGEi2N0d0syF3b97jwYM9wjjigx/8IN1+dur3Nk7T6JL5fInEd4yCwMtUrXPEUYpSAWVRUBd1G0MMoQo8nPR4jJABdVl5eCyilUp6LpBo//8DRsTxm8Dvge0J+z1FHHzx8HFYKYAU31+ERIZtJ89gam/r4HtdZO77RUmCtiPjWruVcNSmwTlNEAsGZzImD8ckacd7kpUmDLw9ptQ1aRCSpjFSBmydsSgEZWOQOOIwwhlLtxNTlwWmVpRlRRJnLOY19/dX5JWibiyRtQRRhLF+wRaGIVVVkcQxm6MB1XLKaj5jHo8RgaA76pNEIY1pCFVAFHeRQUhtHVI54n7GZLZCOMeg32UwHHL3+ARUSDoYknVSBsMu+q17bA9GKKWY1BN0XpKEEcoZkjRitap46603EALyEvpyye65F4g2N5neu4Ozmr0H9/jS17/JX/3sX+HVV19n7+CY3/29Q7p9xdNXL5F1Iy4/nTLcyUFUZP0lRoOzCl2HOCuJk5Ag8N1IV7s1Rvgv5LA4bFv3fHyM+UVYa+xbFxnw4D5hwYU+7tYKfNfXtkUc6Y1TPu7W2yGsdY+k724dPwuaOWGa0c26jE+myJYtVJUNGEiTHk8/9SxHd27RzOdEUYLqOD8voFitKlbLAmMFvd6AyFYIoehmQ0ajTcIgIkx8goWHaFqapsYlqYf3CuHp9H/ceWmfb+t5dL1+McawtbXFO9feZbFYUNSWeWEYbmwzn+8z6nXopSmr1Yow8M9Way1V2ZB1Y65cucK3v/0KMg1YlXPWENLTdVB7Fyvl1TbrX6/l6dZqhPCf+XFJfZZlzJclcRyTpBEqDKhqg7Qa0ayIg4A4jnDO4pzECYPFYKylMhqNRVv7Hivc+3E41jBW49NS7NoaGrSbUK8Yw4o2gTBoLSYwyraZVzXnR5cZhjFJfIa35u+yJSOyssuD7oQz6YuIBUQk9Iyk0isaW1PpCl2VPDzaIy8rxrOC/ekcgE4KKoDpFAglopexGB+yefYyWhvKyX3qowfULuDsRo/pZI/zmxvkd18jcTkEIUaC1ZIyL/jwxz7OF7/8+9w/nlAUS7T26UFoQ+Es3731JtU7JZ/90Ed49gMf8ooZlSGtQWabnoFkHf3dDa7dv42YXWf3pZ8gahboQcQg24XGcTybs33uWZQrsbYhHnXJl5qwc0wQJiSdiySDF2nKKQ/v3EDThXhKiEQ4g3CKUICKFXWuUZEHaOcVhJl/1kopT1Nb4jQlzyueePIypqrppwlCSg5vvU03HXLhwnO8fv1riK0NNmvBQzNnWw0xaonoBuTvruja3unc4xyIWlCFFflhjOiH7ytMVmvD8XTZMiILnDYURc2qrGhOxlTazwd54V+XZcFi5Ys3Te33FUY7zwtriy4qDAisablo3vpstcYZjVOP0jTX9/f69XsP9xjnzyfw4O2GFuZ5zslszmy5YncQYLUHqp8yIL9ns7h2Za4VCuvAkUgFHvKtBHEYInyVH8IQFwiUclgsBBEqtkRZn6R2JCcdmqZiMZszm02pqorpYo5t/Jp6a3OHs2fPtuu9oFXfaZwKaIzneknr5zRn/L6vqWryPKeqNaU2HM/nTIscrcAag1ine37P8R6FKwCeA/P4ex1Q6YZaW8aLBdPZgmgYerVTq54Nw/jUprW2uWgR16wAACAASURBVL2v1r4/w2FF+6hor7FFPrLR4Ys4pn19lNT76BBWIJUiSSI0BvBMpaha0WhFU9TIbkASx6yqkuVy2e7lI8q8gMbbiMu69HN24xX+QbuucVL6gqX0hSaahv+PuTeJsTQ7z/Sec84/3/nGlHNVVtbIYrE4iKLEVkvq1tCWIQo2oJVhGLA3hr3RxgtvDMMGvLIBb9sGZC/sXngA3EBbsNQammq1ipMoDlVkTZlZOUZmjHf8xzN5cW5EZhVJUWyJJR2gkFmJiMwb//id73vf5/WuwThB17VYowMSwoZkZEcQe3Rdg4gThHdo3dC2dUjG7DRaGxaLFU2tsabi4PExKorRqg2fTzriPEM4j+46nAn3J8ZiOo1uDU1To7WnrmvqVmMs5HmBioK9WyLQG6ZV0ssZTsZ4JFle8OILL9MfjjipGxIf4YxGSP+hd/KPtML5cM4ET67XH/W1fxvrJ2rieDadPxEKP60t67oi3zBwpJRkcYKzegMmztBakyRJmFp2HYfHR4wmY5IkYT4PkdRRFBFLRaM7bKc/9LDzKhQne7sX8ZsO9mq1IMsSdnZ2cJsIxiyN6WyMM4aybTB4oixn7aA/3iEfb/OJS88QScXx4wPacs1iMaNu1ly9dJ04Tjk5PeV0WZIVOdPdC9RNw6JsWM6WRHHKdFuiN4XfGXQwSwJ7QSJQUULRyyh6MaarkEIyyieczha0rcF4SKVEqZRJssXaWbRQzJYzZquSh/f3qQ4PuTjuMY2HqH5EKgROGVyr6aUprz07wjGkFwvWp8f4BKbPjNh+6SV0lPP9N75Jf2vAi59+leefu74ptv3mM7esyoYoasJxlwo25+bsYjPGnMfZnf2czjk63ZDGGTZyOB86p8FHGpJNYpUhRfO3eW3+2PVXdTjPbhy5kbE6555IWl0AoF7Y2eXk2Us01YKH+wsclizNEEZyMlsQxylXdi4zGIyZTq+wahw95bl85TlqbTk6OWU8GHL7Zknna3r9gqtXtqnLFQ8fPMY56I+GrJY1UZawe2nAzsUeQsBs5SkGKWmsmK0W3D94xOPZAlnEWKtp13NSpcmSFNvW4DuUAm1aqqqjazu2x3v0iow0TohFgu5a6mODzVoK1UOkMVZ3tOsFOEOvSNjemiBOHPOTQ7779RV33h3wmc/9LK+/+Aqz2ZzZYsXlaZ/VuiRNUwajIYPBgMV8xdsffI8/+9dv8MpLn+Czr3+W3/ytL2F0S1U2WGtZLObcvn0L6/S51UQQkSc52xd2kSKwcvZ2L1CtV5ycHjAajQBYL9bhJWBDXDM8vQHwCAkyUkj/8RV9f90lROASfaiJ85QS57xh46MfaOKIjW3q6T9zzpMKTW8s8CJGm+5D/16AhXL+HJZn7BwIwFDCFE94QRxbFl1D5xrGr045pCFejtEolrM5RRbjdcfepYt0Xc1wlKEizzOTHbrFmpXSNCJBlzXr1ZILkyFtGyOtwIuU/nDE3f3HuHSbrctXyE4OcbIIIFhnEFLSHxTYpiFJI3R1Si+ReG354L23cN4gJcSRYNDLyXojuq6lWbcYYLq1Q7Ou6ScRw16PvOiDUkyHQ0Q8JC12aDuD1Ybf+LVfYbFYsFqtsPYqdV3TrFdMt3pMd6b8n//HH3DxwrMI810mg20uJiWTiwV7P/MF2vkB/91//V/yO//Ff8uLN17gD/7onzMcjRmPr3L38Zz4RHD/7nsAKOXZuzBlOp3y3PWEvUs5o4listsg0hWOBOdTQGKNesIp+LiWACuDK126sFGWhGhW6RTeixBv6w3CB8UAwoVEh85hpQOjMNIHLoRvEV6A9WGyqggFpReh4SM8jdgUwsKfR5O7VGN1R1U37A73GPX3woDHQ6s9cZJw6dJzXH/+Ve7du8d8uaDtOhaLBZkxaL1gNNwDPFJYIq+RMuLihStsb10Iz4sNcySOFdZphAgJkkIGi7eKAjBdbDZY8KThdGZh1Dp457MscKFOT+dorVEypjMhwebR0YzpZJtYKsb9ANcU2PMGdtM5qm7NKy++xEvPP8/Xv/5Noo0NQGyUL8452qpkuZjT7u6FOOONyi7NYhCOtquRyuFl/OS5oQgsI1/RK2ImoyHLdYmPx3hpsdowzXOySKCNxQhLKwyNb1noFUvb4mKLlY7OGOzHqMRhk0yDt1gcVgb+UiQcSji0tkjR28joHdYasrTgev9lvHL4bs2+PeK7iyWx+C6fUM/R6wY0t94kfXWP8TKnSIcgNG2z4Ojhuxw+ukPTtRw+esTRbMnJynKycmR5eCYbJ6lqR5J60iTmtddeJ59ex+s1GIduW1Zti+kq3nrru3Qi4bUv/Cov/cp/SJwH5kdbB4XDalXy+V/+9xhfuM7/9s/+J3S5xqlQG4cYXoezLZd3d3jxEy/RypT50YJiuk1nHEnbknuDtwrVNlz70n/GwVf/gLvf+wrOxlx+4UUuvfgC43mfyWTErYe3mc1O+cJrn2J9smRvbw+QeKfQy8OwUfWSor/FYn2PNCm4cGFCoyWN1azrGiUkRSbwxmIsiFTgbBQ4Ft4zX6+YjvvEkacoUqaTCyymjvG6ZP72Qy5ef5a3/uJf8bL4RS66EY9iRX2yxKWKrijYXlQkvQmPeichAjt+ci3HIsYXS+QYXJfxcb7OV2XNn3/zbYTweGuxVhNJRdNWrBZLlJJMp9OgzhMC6xzz5YKq6ViVa1oT7vc8LYiiiLoLKbTWgTcelMM6j9YB3JxlCqGicyXOX7mRkwprDdIFtpozHmJQCczKkjuPD3hwOudiD9bTNePxmDiKMNZupCge9xRTy4pQvzddG5o4m8aK8hALha5bkqRGekUUBcpeSMDaNFyTmGg6hjzlRvQJqqri5PSYti1Rak0SxfT7A9I0p1cMyLICpwIrVVuDckENAwQ2z4Z/6rRhvl7T1g11qVk1Le99cJevfO/73DteorIYV1uSKMH6Hx/5LTcb5rN11vAoK8tcVXzzu29SVg2f/8zn0Sg0YIUkTlOQinVdUXctddf+vRhCP22rh3AOpfAbFY4LCiIhNlHyEmdtSODd2GQNHuVDPuRGA7dRXjlklBD3BV1TslpWAbrtHMZo7t7+gEgpHh8f89Y777J1cSdw4JznwaMHDPLkXEl2xlZy1pyD6SMhiZQKwg7foqJgwe7qhiRJiaTi8PCQb33nTUaTbUajwSaldEKUK+Koh7Edznvm8xnHp3Nuv38bbcIQcDF7h7yQSBUCk9LeZZJNgpbWGqcN1brEGUOEZLVasOpW6Ba6rkVGGdvTMZefuUZrdBjCCMX+w/to01KkY9544w1Oj2e8/pnP8rkv/Dyt96RFsJXJzd44TLw4H1Sdcyr/inv7p9nAgX8LJU6s1EYOK8IBbFqyDRQ2qFYMuquDVNmFKVeShOhgZEhEqqqKOE2IZFCk5Hl+HhuexUkoipqG4+NjrDEY0yGVZ2u6w8BvFCHW0rQVRAqJYr2B68ZpShynxHH4u+MkYb6q6OqOtm1JkoxisMVwcpnRVVAyQeklXVVSecgHA/rDAJaNspTTuqI3GiKU4vHjQ+I0QXmH2iQ4ndmplFKkaYJSIQ65qTyL+Zw4ehLpN+73QgStD93julqTJzmH5SG61szWFUfrFhHDZT9hPB6RYNDuhLJcI+KEZ8aKx4cPkY1jdythazvi07/4RRay4Otv3eSbHxzx8//kn/Dpz/88MoqZz2dUVXUOmj2bAlhrQ0dTELzQRcHp6SllWSIRIR5yA6FOkoQkzqjr0NnsD3sMilE4h/GTOMG/b17BQLKPcE7iXYBuSxnhxCb1I01I8pTFutkMbA1VtSZJMgbFiKZqWS07skyS9caIKMeKhOdefJnLzzzH/bu3ODo4YufiJR4+eIw3mvF4yNXrl1i3JYtVzbIqyfoF/X7B1t6AyVZOW1dkkUTkGafVEucC/0M7S24EpipJfEvmDa6sWR+f0BqNVEmI+UsTlDHYekFrlogkx8oY6wTaR9TNkl6eBZK8LlmfPsJ0M5SomU5yxqML1HVKfxAzHPY5uPuA8rSi6A8hglg4hnlKWa959GDOm0cLHj58RF70+e1//7f5xV/4R0wmI1SUMJudsFgsuH//PlW9DHDoTXqCkIqtnV36vSFpXqCdBm8p6wqjNZGM6Y96GGc5PVkSFRnTrRHRhokjBMTReZ4J1gTPbPQjput/V0sAyVPNGI9HySfT9CcTMv+DTZwfYgfMeimZ66HtjKU+IYsGH/r3nHM0Z5wcwnESQhDhQz0nwkvdCdBtybg3peoWaHvAZO85ZivDrKzoakOnPXmWsFiu6dZrMH2Go4zeSBIlKWmuqVYVWS9nr59Rrpds70w5PDph99pFyrLk4vNXObl7yP7BHfq9HiQD1us1RRqFpLuuZm88xroWqTSdg6pxpEVKGgdlQ9cGFWUqbSiGvWW9WnN3dsIgL5hub5NnEcKX2NYiTUMjSiZFRD8RJJGj7A0hThjvbJ/bc1OncCrmj7/6Hd48WHJ3eZsXLk8p94+58HzML//DL+Jmp+gk40u/8Yu8+/5/xH/zP/wvxOmA1bpltnibvYvbVNWMVRUmjWkWc/fBCfcenvLBrdHGYhUYb0UvRyeasjwBJHGU8vix/mldej98CUJjJhgcsUKS+BAtHuCBMjBtnEFIh1PQCoPB49KgtNQi2DW997jIgfPhShUheU96QY8IKSRaeGKCTcZGnjqyCG+wXZ8smbA9uURrWh6fPEb1Iy42C7JkQN1CmvQ5OZ2TF33WdYN1kPX6uLbE2JxEhXjqJIrp56GW2Jruhfhe44jis3ePR8nw7JEqxgmFR+Ad5/LtIDOXIRLd+g9BMoUQnJycsF5VnMxOKXoDbt26ybe+9S06IvYuXGZcFGgM7713whd/9nPgOVcZz1cLRsOc/UcPeHD/PkUS0zQ1wJMkFRXiUteLeRiQWEfdWebzOTu7W+R5Sqdrmtagkt75Z/M+bIjGoxGLxQO2pyMGgwHvvT9DqZjdXk7rOqwRpElETUdlW1auZGbWNMYRnMf+nJ/xcS3vwTiQGPAKZQn8OmVxLtRu1naACg3FKOa58Sd4tDzkxJ1SHa8Z7I1QQpH6nM5rZodvE+9FXMmeYySfR9ma1f5dbLeg6+YkseJ0NuPO433WLaSJ4Oq2REUpRrd0XpFlPW4eGK48/zMc799Dr/dRvSHWWPo7V2jrinVpee0XfgtVTOlffIU4S5FJUKBb5xBSoqIMlQ0YFmN+/R//Bnfu3OR4fsI3vvVthLS8+tLz7A12SYSlmc1Zz/dJihytDRDg2PgIRNhAT3an8Llforz1JhQ5a9OxNZkwHfaxly6TZpL1suLeg7sMJlPWDz7g+rWrJEoxW5UBuo0nTgsODw9pjWa7F/G2qeg6h3cSIolyQaQWJZt72nbEWUKR97lycRspPIMip3OaWdwyzC7TRjXZ5wq+9f332boy5dvf+GPGFy6TrgzzwvBYSKZJx76qGM8WHFZzLmYTWtOh/Bn7zdPPR8Qqo+osH2fR2LSa+wdz4iQiiRTOatp2iRIBpWHaDiMXxFEamrvWcHg6w5igYAPI8xxnLE2nOanakJK7UXLkSYpUEMdBWRclMc0ZP3ITRX62F/joEiLELMPGdmW7kOaWJrTA4XzOu3fusGPHbG1tURRFQCFE0bm6zm2sNQBOCZwTiChAhqUQfO1rX+Odd99la2uLz3/hZxlkCUopirxPlCZkRU5SFFgBPkkgilGFJJteJB1Z+ls7mK4K3MjVkjzLkCLa8JwFQrGxi1l0VdPJljzPkThM22I6TblccWpqpBUkKqfpNN9+520ezpagwMmQchQJGdKVPrLO3Brnwz1CI+fpdo9QEqyjblruPHhI22rywYg0TQJcN8+IkjS857xFW8PJYs7HPWsJH1YgcAgvES4AoMVmABNUVhKhJNIBLrzTQyhkGEgo8STowrvQWLDncfc2mKqcQ0VgWgfOkkRZaLyNRyR2m1jE7O/vY63n4Qd3+da773GtrXnxxsvEKsKZhqa1dCakNGodgnqU2IRTItA4vJRIBM4ZjPZ4JxBxSjacMByPMFKyXJcsTldMJhOEh8GoTz5ISdIcFSe01nDr1geU64qH+4+4f/cB1lou7ezRKxIEHZ944VV6UQ/hFDiN6xxtHdi63mqaTtNqQ9NaZouKOCmIB32mV66xs32ZelkhHfR7Ke+88z2EgWZW8bWvfpOvf+NN/vPf+R1W64rOeaI8odGGNEkDV85yDhW3G2bnRwHN56dWyXD8P1Lz/9Cmzt/g4vsJd0OhG6U3EuSQ3a5DEyZWKERouIj0PEceKTgT8J4pIc6I1lprckKnLUkSmqYhRJjX1HVNVZZIGb4viZNwwyrFcDik6zqapsI4S55mZFlGWZZ4q5GxIo9TcOHkmqbF6A7ddSgRkScZXqiQgtUZRmmMiKNQbEmFdYSGhTUoIemMxtcVSRZkYFYIeml6bqGQmws6ihQq8hjDOZiw1SGlIFWSJEkwukEbEzqXUUQuwjau6zqsd7ResKg1y3XJ1nBKFCcoH6FE4A1EXjKINFkac+3alME4JyoKbt55yNt372OihGdfeIUL154FqTDenUvG4zg5j4vTWp832c5sblLKYJt46qKMomgTFR+aVpGwRDL+EGzs7CX196KV/dQKUDCHVAJt3IeI9t55jLVo09LqFhEp+qMBuunQdb2ZoAcFjxQhlrLzFq87BoMBN27c4PDxA27d+oDXPvsai2UZVFxRwmDUZ7I9QaiY45MlWztjev2CvIjompquqYiUACHp6opEDcEa6vWKPBrgXUfqJTGWtqqpyhDFnRQD0qiPjxydhKZd0NUenZWkaYjDra0Eo6iWMwaZREhHp9ecHD3g+GhBLCImezuU64iiF3P1mSuMxtvcf/iYum5ZNyWaIEdfrhc0dYfuJJ9+/bPcePY5vvhz/5CiN8BoTVWvqduO2WLOcjUnScK1VJYVKo64NJrQH4wQQrCqSuJY4a1F6yD+lCpmvgw/mwW2RiMmwzG9NAkvtqdsKN65n3pH+992nYGNn35IR5umztP/OfPhFL7w9R9W4QAIW2DWY3rDlMY/eAJNPvteQH5kSBViF0XYuJ7bqSDCI2yIO41si9ESLxTzxSlFJLFCYp0MTUJinFUIUqq6IyLGi5gkK8iHxWYC3TAcTViUa1Qe41rYf7xkEEuubg+ZdQJtAg9AEKJWi6yPaUtULBmOR7x/+yEHBwuyxNDPCtJdRRSnoMB0mjRJkP2IKElZrFY419FWS6QPqs6ubYnzKftHx6zMPoMsZaeXsVwsMcYwGAzI8py9rQnFZIf7Byv+xR++gcoyjuo1r8fb/Kf/yT/g+c++zvZ0i/XDx0gMIpG89MJ1hsMhjw4fAZAmKV1pETbB2mDLNLZDRY4kibCqpek8toZV5YmjEG2ptUEpRa8neBLJ8/EtIdgAlj1IhfDiyTR40/QjViFjMdokPuAxzgTfvRR4sQFxspnobSxKEkEkBbmINgksnuDDEnTSo2V4vkZAEgs6s2JVl+AVI9Gj7SpileC82qh3Q3MpUQIXCWLhcbEiSSK013gfM+gPyGI2qt6zH9LT2Q4nHEmcglBYF1Iuzu4Tx5NGi3NuM50UH5oqRlEU6hUU6/WaO3fucXB4HAYetmM0HCKs5eDhfT758ktUeObzOaONlVRrTa83YLE44eTkhDSNWa2W59aCs6dWGProcz6GNWFDclbvZFmoQcD9gPVCiQiNJssSlmWwoo7Hl6jLCu0lrbcYZ8hUgEVqp9EYNAZjHCraGFf+LriMziFkjBEe6aOgpnQCh0ULRwi8j+iE5dneDW6d3qbSa9KsR5PGeKdBCJSTaG8QwuEHKa0X0K05PbpFlgjKtaNXDNm/cy9ECUeC3SxHI4hlTGtaXJLRypx11RInQ26+9VV+5Rc+Q9M4vCpJoxhdzZHDa4y3U46Pj8mjmOmVy6goRsaSqgs8E2stjXPUZUhNFc7Tz3rofsfWuE+sIrb62/SGI3IROIJtVYf70gVVkowj6q4lT8LmVUpIUsHx0U1Wdc14d5ve+Aq+qyhyxeBkhkoO6No1aT5CipaHB/tcu3qdyXjI0dERUSJZrxaMx33e/P67XN0Zk0hF5zQQeF5ISQ8X+GkqJNsY01GXK6oyMHouXLzIOE6RRdgEiS5mPUp55ZMZ737vNvLaDpmLSXQLQpFKRWssfRHYmWeptXGUsJ6VDAYDPBYhDVIIkrj4eGtGIfAqCvzGTXqfijKMrjebehmgutaAhartaLQ5r4PPkBDLsqRtW6ouwIOVUpsk3hypIIXzJpC17YeU+z9KPe43dhk2SUkyIO+QkUI5hW5aTk7nnPYcTdOcM3Wy3qY2cB6BDyrEMw6KFJuUvWDpNG3HwePHNHXNjRs3SMZpOO9lGL41WU42HOKkQhQ5IkuwQpCJ6WZfFPg/zrGx3J0Nsl3g6hCcEsZavPSkaRpqI6FAgRXheRsnCRGK9WnJ46ND7j98hA2zBbwN18aPwsiduQqeDMF+8BKy3oEC7SxaG+bLFTdv32Y8GeFc2O+1WodhvFJ0xgRBwN/BEnJjt97YTPEWCIr0wCbeNIs7h9ocFEvg1n3IHr1xXEgpn4CupQrXsjB4L3C2wTuJsR1t1bI1uYDynqpp2XnuBZbrilvHJ+wvVozLhqIo6OUZvquozixWURz4Ss4SpynCh/2RxCOwCIICHw9WeLK8B0mCQRDnBdOtHRZHR1htSYWCzmEax3IxQ0QRrdMczE6oypr948cczY+JVMbENCRtqCnSKEU5hTdgG4PTLlyXPvCgGl2FpquSrBrNKItApQyHU8bTKULbsB+PFDc/eDckXtsYrQyD/phPvvY661VF3E9BWwqlApJCeMSG/eq939S4Z9fhxr791PIbdfzTPr2fhrXqJ2vi+DA1yNMonOBej6LokSQJq3LNqmtRCJQMjYNYRcECsWkOtDp0UNfr9Xk8n/eermlZpqvQvY5k2OQ5x+7uLlEkKcuSuq6ZLU7xThBHEZ3WGA+jrW0SFVHWNVEsKdIUAayXM9aLU9AWNlaHSCh8VzM7OKTIB6Fji+S0WbNYzqhbA6LjeLE+vxEG4wn37t2n1+vx4ssvh4ixzYM7MFQUSRSFh6RtgOQcXNbojrbuyNI+QkjKpsY0NU3TkCYx/d6AFI9EMJ8v0R66HGbAB0drRHLIs3tTLuxdJI8KwDCe5ETPhWM/3O5TmY4//PNv8Xt/+YBGZHzpP/iP+dwv/Braez64d59EGfL0SRPm4enBeeE6HA7PN5RNEwjtw/6A5XqF1YYoDhBP3bYbYGiOlDI8qLVFxRG6s+HF/CGf6se3fuxN4QPX4elUAOcDabyua9pG460nThN6yRRvLIePZ8wXp5i2I4krjmen7B894JnhLiqW5NmYX/9Hv4L0jv/3n/8Be3sXeen5TxKnPQ4OHxI5z43nr+G84PhkSSwFW9tjhkVCs1zi2ga6Bte2XN2asLW1xU6RYdczXArCGrLY4ZuGtlmTECbOeIP3Nb5rEa4GV6G1wTlBWQpknLDuDNoJqtUhtrlKkin277/Hzfe/w3JRMhldIkste9vbjHfGXLl0jc9+5gvUrWM2WzBbzSn1AiE8RgU7xYWtK2xv7RLHMXGaoTvLcrnk/r075/bHfr+gaTrm8wWDwYCLly9x8cozICXGgcEhXWAF5HnOcjnHGEPbaOo6TLPkJUu/3yONk9DR96HIPDu/UqV/J9fYj1sBzyk5j+uUArEBHT/d2JHRE5nsE5q/QQi5mcIF68QovcLX3nqHf+dLr9M8WIX4Z+83sD/wWBKRbH7/RKrtrMfZiDA0jBAo8nSbdWuIE4NoJNVhxqqec7AIqqhRL+PCSFGfrtnr9ZHDiHfv3uTaZMJoOwfTsHVxhNU1ulygdrYxRY9l5XE64fBhYCkdNLBqJD6SGGcpipxhqpDOoFxDrxcj44y3vn+fg5NThLdk6YDTVcNq/YA0AqUEqcsothyXfMZkuE30TMbYS2Q+YtU4/uf/+9/wwaOGORWxZDNd6xgOcj6zO6DoJfRGQ5wX3Ns/5tsP5rRdu4HXRgxGY/5sOeS/EhWvXrvMzKzRwz5xU1PtP+aX/+EnOTq8z9mrse06ui4cdyk6rAvxlN4rnPXobnU+mFAqFILOB3WjQ7JYLfB/DRje3+YS0kNikUgiJ0mb8C52OlhJvDCBfxAHC08dNcyjGi0s0aZ4fvra7TZFs4gjRBIFdS2SwmUkBFiwVw1awWms6VQXrj9ZUjcVrTYkscD6BudeoV/s0dYtee6wRtIZQyRh2EvJE0niOpZly2TQp47U+SAh8qFeqLog427bFtFqVBwxGI5IkgzjHYvFgun2FmVdkeUJqSLE3ccxVVPTaAdRyqg/4O133mZrOuTi7h5f/tM/4v79+yQq4k+//GXatqWcnXLjhU/xyqufYmtvmw/u3eK5Z5/l6PQIkUiG4ynf//a3ICs4fHjIX377fe7cP6bVIZbYe4fTDcJLlM3w3lM2NYPRkDhRrNfLkMI2HxFLzWTvAmVdkzgd0sU2/IMWh7GeXpaTxWsWZcV8/YD1SR+zbVFFRxYNONUrdOTpsLSuw3hDow2xSvFuw935OJvhXuA4k9kHPpMjpJlJHEIonPA4oxll2+yvH4bB2OZcJlEcaj4lib2nWDhWWzGJTLjQphyZBwx2tljcuklv0qdSlnycQzTmlb0x+fYVdvcuc+9bb1Ktgxr7zoOHmKrkwvQSydiSFZOQ+FOd0uUXMKJHu7hHvrXN7s4lepMx1ePbJDc+g/AeJSVtZ3DOU9ea2f0POHx4E1Ov2B4OQ5rZJz9LlhVMp1MO796kRjDXLe31Fl2uOD5dMdoe03QWaVY0QjCZTIjwRMUYLXJOD76PNg20S2y1ZK5Ltq8+w3K9x2DyIouDb2N9SiQL3rt5m90LF9F1ibeKOIm5dOkKUib88Z99haZzRdV7GQAAIABJREFUuE1ErgCSyFK7iFgakjQmjjyx8Bhhmc9P2RpP0F0YNk10THV8QjTKuJrusu8XPPPChA/2G96+/R572ztEhaTooBSghGTmK3rbg4D7sY7+sEe1KlmdaLavjGgbTUA/fXzXopIC5Tp0ZRAqYjToI1NJ04bnXJakNF2LdRq85Pj4kHXdIKOYXq8fhnLLFQ9PF3SdhtgxHQ+JpCImQglBEidkeY9WdxydntBpHRo44gmg94fVMBKLixz5oEcWJ9iVCayXpabF0WrLd2/d5YLf48bpgt50gFea2BMg6ipGUoAPqV8q1TgLnoSmC5HRr/2DX2Lr6jVODw9QkWR87YXAIlzMWFcVp3WFPT0hwtOXEdMsJ41iWn+XSCrccoVsOiIZYfBUsaKyHU2saHFktuNoNidOM4rpFNnv00UJSV6EQA6ryCeKQiwojeM7B3f52pvf460HByEswEvA4yJDhUa5KFjfhAvPUYJVqtXdRkmZUKEDE1KEJNoIQXAFBgtXhaetS9r3vs32ZEovy6kHQ+xkiyzLSIqMw7pkvizP2NAf4/rwtX8ud3ChGWNwoZb0gIKzCG7JE/Xo03ylj15X0tlzhZLFk+cbvqlNqVYr3njjDVLhGY6nvHvrFmVZcvO99ziezzg4OKAxmiwS1HVLEscIJZkt5rR1E5phgyFJGpHKiEEvD3+38zgbmIjjfkExHG3OrSNWivFkyPL4lKap8SqimzesjluyLKMyhrKuuPXeTRarBXXVsbu1S5YFVXVR5EyHPVrTUus1Qiiqeh0UP8IDFudM2HtXNcsmcBijNEF62N3dZdjrcXh8wJULeyzmM8bjMRe/cImuNdx48UU+9dnP8d7N93nh1dfDPbvh/yHCkFQRAQZQgUO0aaY9Hfd+rsg5G+Bs3rc/SrHzN10/IRMnTArURjnzdF58Z8+kgsG64r0PQN1Nd1Aoia40dducN0DiOD6PIOv1DDtb26goIRIaJ2SQF3cdZ2A/pULiVbfxXRrviIlwQoKSSAI4r6krhLPEkaQzLcJ2ZFlBnEQ0bUvT6k38cUJnNFW3Qmu9IYBL0iIJlPUoOVfURNEGzCUEkRAoH5gycgMlDA2u+PxkdV1H2wYZfRRF4cCZoOIBMDrcXtYGZUzXdSAl/emUHIvWS/ZPZyjp2BruoIoIpWKivAeqoJMpDw7XHC5O+cabHyDliBsvvMrPfOEXcCpltZrT6gbTtMRRgiakEpx9ng/9TJtJgxIba5QKf54m2bnSJvxMDUIIiiLedPgFnpAYkmUZQi7/hpfjT77+Ol7Ec5jiGWNAeJwIkyLjXUgtMdCaNsjWJ5PAK1iXVE2Jk4pytaIzDbHTKBFx44XnqZqSr3z13/DGn3+DnxMJzzxzlTST3L5bEQmPFTB1IxIJvSzF6oauqfDWkccJXgVF1TCLKBKJaBtMU+G8CSoq04HRxEkEQtA6Q9doOm1wXuO9BdehTXhwWRHRbgCRtm7JPeTDHKNXOFujTUNZLUlXMVvb03Not3eC6XRK1h8xabbxkcNLS4tBETMuxogopqsrTmezTQSgpm46dBfgsULJzfWhuHTlMtPpdGMjSEE5YhSRgkid3U8JKvfUdRnsQdpQljXL5ZKmqwN4b3POvAgvrqc5M3/f1kdVNz8I4Pvw156toI4LE/hON+FZWpVcf+YysYpo25Y8yX/sC+DspfHRZToLVHSNJunGzOdzlos1XacD1LJqOHGaYS9h2bTI2YrFYoXc3uVwnVCVaWCf6IhBtItbnzLvOpTwHB4f8fj4MSufU1mPsQbTaiajnEQE/72KQSUxSw3r+YIHh8dYa+kX+ebdoEhiRSTDVEwMEpTpuJtYtk1LoXL6XcGXv/ombz885P2DFV2ckergR06kCgyKpuWbt2rOA9kV1Bb0BhztN7GoTdNgxV1+/xu7vP5zj+g+8Rm2rcZlGdZakqhgOh1xeFo+dRSDx9xjCRHyNky2Npa/pxvEwQLzRMVnbYDLftzLe79hIQgEKryeNr/aMy2GAITHeoMjTM3ZqL48nE+WzrhNQaka3hHCi40KR+I3QFQvwjtREdLSlExIYjBGU1ceax3DrUlQ626GJHWzRmySrc4AvzbLgirIBDXTOVtBW5yzpDLi9PQ4pPVMt0mjcO7KpkYIwfWrN3j4+CE7Ozss1yuSKCHrZ8wWYeLsrGG1WKDrNWki+c43v8HD7W2yJGY9n/Mnf/In7Ey3WByf0O+lHD5+RJRkfOLTn+bRUcvdu3fJez3SNKVqOpSQ3Lx1C90Ei7IQ4rz2+YG4WO9JVYTbcARVpJjP5zRNw3IpKMahoaCr5kPfh7Obgt2dPwcsgs4ZjFO0aLA1EkuLpfWbAIbN9znnEEo+mdJ/TCsUsRIrXIDvqzAIlA68cOH3UhHHffq9MeXiATLJUA6c2gBhCVPPjg5fQzyJyZzntF3SGw7Qi1PyvTF0HlPAYGuMnI6YXr3E5QufZHb7JpFMmYwKZrMZnbbBIrM6pNjZoUgzvG3pkm3SSCHqx/S2d6ge3Wb88s8yu/t9hs99AScglQptQyqktRrXtaS9KS9+5uf4xh//C7KtLeaP7pJlQXXjJ+FcLcolV/de5OT0gGR0gbHpaJqOIZLRaBRgzlicE6hIMH35Z1guD5jd+ku2tn+RbGcPuz5hdet79HavcGv/HdKdayQuYv34PfI4oms1TlmqStMTguOTEyKV8sqNq+zPb9K00FlNloTBSp6AcDHKR+z0C5SIyApBFjusEhgDUVFQ9yKm+RidxJzoY3YuPcvy4B5RdkqxNaRazhC729hO4zH4uKDRnmLYO7ceSCHoj/vUqyX1uiWKMsjrj/la9OcKN4unrCuKLH9SJ26eOc53GO2oquocCg/Bltk0zXnTXshQl5xBXaPNsLjrOjodWB3n//aPa5x6j4okvSyn3+/T1HVQ00rQrQEcddcyW61YlWuqpgsqVxua9aEm8CEoYaOS9xuXQLoJVLjY6zEZFJzs7lCvFxRFn6xXIOOEtFxhrGZxfAzG0tQNp6sSKQTRJsnJtoa6rFAbi24bgfaOmalZNy2ybYiznEGSkuc5RVGcD7odwVERZRnGN+im5MHDRzw+Ot7oS354bReUjBs7ylN/FuxUPyB++FCddMZuCQp8S1XX5/DbSCp6podsKk5PT2m7LiiA/g6WfNK+OV9nSo8zps1Ha76/ThPg6arj7PkfwkQEw+EQKSX3791hXIbjUm4sSb08p9Udq6qEJLzH4jSlM4bOhAGKtub8MwslA4tHCOTZfjmLSYs8pEACrgvQ/jyOUZHHtg2dDY0hbcJ9sm6aALtuG6IoZjjMSLKcNA6ikShJsdLRtuHzxmkSlNldtbGOiU1dZmiMoelaVDYIzbpen6LIaJqGrtHkecqj+0u8FBRFwWSrR1W33Lp1i5df+3SwYMswlPTi7Byw4TQJvD9TRT0BGH/03Jyx935YXf7R58Hf5Mr7CSPGBc4bvA0d/a5tg5qhLQIrRimkD+DbSKowaW/bc+hxlMSMx+MNFFmfR5MFj6NB247OtAEwmxUYY0IUoLUMh8NzNclisdjEYMsgY/WSNFYBLnp2aUkBkWJrb4eyLLHOMisXYVNU9DlZH22kgQ4lA7k8z3v0hz0ilTAej8mynHfee5eLFy+e27XGwyFZkm5sVAphQ6c4UjF4y927d1itwnQ2zlL6eUGUhCJUCIfzgQ9Ur8qNfUzQVHWIZd/bY+faBUZ5xtitePT+B7x1cMjj4xnTniJKE0SSsmoc2njWusVaz6VXPsNv/eZvc+36C2RbV7iz/4BlvcA5R7laU3ch5SP4dWPiNMHaoKY4a6ZFUp3LNMMxCYkGum2xePpFRlVVCBkhlSEvBjgHxrQIDHksz6VlH8s6k41+xG94ts4k624DdUOG84XzG+1TkL/ZRuONBB/T1AZBzGg6CaDrkyPu3rpNY2oeH35Asb/FaDhn3N/h6tXLXLl8Ea01//R3f5c33/pdfvXXf55Lz1xgazBgLVu8UwxjT5YmrMtTdNtw+eIlLkx3UTZm/9F91rNDdLlArwyPbr7FYmtA0e8TjXNmxwe09ZoIj1KCqjE0rQ5QUQfVuqRtG3QX4KHOgfYgpMTYlNP0gNE45/K1i3z+s69y++ZDbt16hNE1129cYrK9RZb3eXx4yIVkSC8f0B+PmJcLynKFSBLSOGE1X9F2Bm0Nx8fHdJuNgcXSmaCkSVTEdDrlmavPsXfpIiiJkBH5oMB5gV4sUEqSJBF1kpJmBaoXb5KRLHUbNjLzxSm6a0l7wbR/Bk07Q5J671Hi71czRwhBJOPz30spEUTn05IzC4vz5vxrzn5NZIFzBqs1/WwISI7e3ufGp1/hzvvvkMVJkKqKAI1FgHees9yLM6sLsKHJhuaG31z2wmdk0ZxYXuTNN3KOHt+jbDyuCzOGdatZnrTIOKEuZ/zS66/QlpJ39o/g9JgkUthhgl93dFEBRcGJrliT8e337lM3iro7S7CSjHsJmQgvvuOmo+4sB4sldx6eICUMi4IYj15WpMPNFFOl5LEiiSTClqjRZUZGYAtD/427/LN393lz2fLIO0zRwzQlFwArBJFzxGnOoqpxcU7lHZ2xmwpGIHwoEKSQOB+SMaZDwT99q6H+H/9X/vv//bM8yLbox5pqvWTQy3j2+lUOT7+/OahBPgtuwxwIwMuzQt77+EPnOcSPm3NZe6TkDxSaP/UlQuwmOkBklUjwOsGrCCPPppseF1mMN9TSIhJJLEHETxr7Z4oN5UPwQKQUkQjPzUhFFDIj9RFoh4gkVgl0YqmMw9uOUguyPEyEVbLDoLjKtcuv4QmN6aOjUy5ciIlUSlOtw9HeNGoHRbAqnQ0byrJEpRLbWO68+xamWhMpRbyzi94kavQHQ/I857vf+y5Xn7nGyewUFac0PsIbjxeSW++/x8nBPl/52r/m5Zdf5vr166SqY//euzSNY3H8iJdvXOPw0WMubA3RVpNkcPO9t/h//i/41V//d1l3FaPRhHfeeY9f+7Vf4V/94b/kT974Cnu7l9h/8Ig0yTBdGm7CswQP7wPbJ1Z4LIeHh/QiSTTo8XD/PlujhGeeeYbFyTFZ0SNPMySBa9Vpi/MGo1uc6cBrjG5Yrztm6xVquIszD5FuiaRHY1us9NTeUbYdphV0pkWmgW/38dqeBd5FyM1WzJpwLwkhcDbAjJWIscKwv7qHitJz6570kjiPWc5n9Ic9srTHyel9iqsvU9SSsleilgJlBclwGzGOyNhm67kXGJmO7LBh/95bjPpTnr/+Mu98/y3WVYluDXnWZ9ZZXnz5dS5cu4a2Ec3+m/idG9jZY0afeAH9wbd48P2/4LnXvoiY7OC0w8ch9avtGkzbkWc9Prj5LWaHD9m58Rr3br/JrYd3aXTH/v4DfnF7RDreYrx3iSbJ8Msl0f2bXH/5xWBPbdYsFsH6MRkNcHGKtp53v/5HRGqKfvZzPHzvL7DOsDeZsH3jNfZvvknVHHHw5h2KoggWbWO5tn2JWPSJhorbH7xLWS0QxDRVzbXdPnce1VR4ysaRKejFHiE8bavZXxheuJyQiJRrl66hYoj6CiscWsGdzEI1J4763FUlYmfCz45HvDXIad5+SOegJyQLGSOE4+GdU/Iv3ghTckALTzPreOH6NTSaWhtsK1Efa9akoDOaPAthHuuqotrYaLIkRIGH4dSapu7ouo68PyDJcuq6Zrlc8/joBBFJlIro5SlZnBCriCxKcTbskZpNDDL2iQU8SZJzxuQPa+gIE2Dpg2LAc88+i2k74MyFMEOpiMVqzl/ePuXa7RfY3tvGe0+exBRFgbQel4CPHHJjCxNSksQRQsWAwFvD3mjAZGuLg0f3KZtgRzVC0htfYO/yRdbLJW3bUi/XNGVFuVxx/PgeXdfR7/c5rVd47zehNS3GWb7z5psIIbhw4TKvvnSdnb1dBpMxKknDz+aDjTRKA/P0rXv7vPn9d/n9L/85R8tglXxyRNym8eef/D/iPBnIuQBodsLivTofBpwd0ye/bpQTG6sbUrFc15hOc2RO2H98TJZlWOdYrFesyi6kdX3MS/hQ155VtArB2VvDes+Zdeej60elnP2opTZK5DM203g85ue++EUeXbnI//f7/5L+cBTg1bMZURTRNE1oQuYJ/X4R9ot1iVMCpRLyNEIoiY8VMo0DXFpAnITGikxUYPvYDmM2Sl4Mg2GPNI1ZVDX1eoZIIor+kKasEEKQZRnXrz1L0zTUdQs4vBd0TYNOFM7FlPWSxSpnrIZEMVgncY1Fax2gxcZRNx2dcWz3eqR5xmg6QWvN8ekRWZZydHTE7/3+7/HlP/1TXn7xVeI45cKlq9z41CeY7u0gi3Dtnu07QnanQzrOw4KehmqfNQw/dMzjiEjKzXn8wWv0Q9fB32Dv/JM1cUS4pYwx1G1Ls1GQ1HUdJP5RjDfBs+mtO4caQ1B/JFlofhzuPz7vaGutgx9TRTwo74EX5L2CPM+JXXx+cBaLBet1iAcsegE85rxh0A9yreVqTqxikiQiUgnGBgiqkQmVKamqJqhm0pRFXaFUjPMQxQlpnpOnCXk+oNOatEiZTKY0TRM2Zy54ASeTyflxKLKcVKVYNM5ZrGhYLCvm8yV13aCdYaQkXeKIutA1Nxu6vdhIIL0XCG8ZDwfMTo5pbMuzV17kmcsXuLIz4NXPzilnM2b371Ou1oEmLhVJ1qOIc166fInt7W0+9fonyAYTlmXL6YN7NOWSZnVC3a5gQ7Dvug4pI4RogorpKcha13Xg/BNby6aQXi1Kos1LIk2DKspaS1NWYGTwHXoflCLqB7viP9Xlf/x04+kGj3gqytlZhfMd4/GY0WCK0R2xSpjuXCBRwaPeGE2UJmzthpt/uXzM++9+k/Fom+eefQ1drUmilN/4zS9x8eqzvPvOW3z3L/+ck0f/P3NvEmRZdp/3/c5wxzfnVJk1d1dXA90AGgMBggRFSiFG0GDINEOWrAgrQmEtvPNOC2+48NLh8N6OsKyQFNbGYjhMi5REizBIYlIDxNAgGkNP1V1TVuX05nfHM3hxbmZVg90iQYJNnoiMzuqsevneHc495///vt93RJrH5HlOvz9gb3ub0dYYra9xsHfApd0DkrjHajrn5slV7t15lW//0ZeYP3oM7YZb/gqJ2Oa0hHfeeQecRYmOQRGlqI5D0TaOZtXQNgbrNNplNE1Ls1oHhpPOUDsxbVWzms555to1bh5cJ89+yMOHD3n8+BE/+7lfYP/SVVQ8YFEsWTQlWZswnZ+ERYiCdQvtpkVogbNQtQ06TeinGqEFb56c4r3l2s1n6OUDsmFO0xrydER/OMFpCSbwb9q2pm1Ch8s7sMayu7MX7t/FgtFwi0u7V4nzPHCIrLmI3nS8W03112kIIJJP7FPhS73r/YYu/Dnc8Skvs20BB06QxdtIEfHInWCZoSNP23qiyOO8C48R75HeE9F1254q9F90BXz4woOwErt6lh++esKjtyuscQgBWlnauqH1DiMk7WLDmW349//vl5GmJv3IFR7cK7k8GvEzv/hJ7MGI7z86YXnvLifTM5K8x+l8E4oV9YJ+nGGtYb2ZsqqDGmw2W1BWLf1I0xOW7dGQdnVCkmjSKMXT0OLZzEuSYQ8XaZrLObvfOuEVccR/Z3v848f3eLiB4faEG1HKo0ePGCBQUVB6VnWQVu/mPWbFE/WM8BABBhE6sE8l8kwXA/J8xv/yOvyPX/g9Rn/3H+IXMElzEPDZn/0U3/jWqz92hkMR/uJcYsGDceZdoO04jom0xtm2i51Vf+o89dMeXoRFnmoF0gdOkvBcJLwReUTmaXVLg8PGkihTSBWKwOef+GID0nbdWOuIpSRWmlzFgbPkJD6SqCjAWc8xi57gOfcGpIvJ4z3yeI8smXDnnT9ma7zH1f3ngaZ7NkmkCoXQKIrw1jEejijLEmcsSRRjTEHTNIwGfaJ+2BQV00NM68h6fawwrFbw9W9+k7b6DIPBiNZY8uE2Eo/GcmVvC1HM+Hu/9mtkeY7WMXvbl/Des1wVbE0m/O7v/i63b99mPp1xNl2yWS3Ynox5543v8yWhiCY79HoDtJD89//kn6C04Pt//B3eyd+hLEvyPEVFGb6twibDSUL0t8G0BpxH4Wmbivms5vL+Fuv1muPHjxiPh2gElQ/XEkLinQ1rKmdYzqeslkvaumQ1XVM3Sx487DP2DWm0xts68BGEoPIGW3usCSxD2xiiyPBBU3GC9SHMfYZzdSwoL7DIwLDwLmzKhA9KNxu6uq1rqcs1W8MBxlnsasMVBkTaY6OE2eI+TEbISFH7FQu7YKfao21r5ruerft9iuNT7rz+A+6+cy80CgqPyrZ56ROfpseGQS7Q7YJktEN7+jrx1nOc/uBr1FZx6fqHKeoKHrzC5PlfwOsUrEYqjVKWN19/FZWknJUFf/ib/yuxTLl/+BAhLdZ6/vBLXyTWEVGkuHbtGtuDCeO9y9z7zu+TjCc8/6lfIemPSCKNcQ5tW5bzM/74ztss5wuu7GyR7D/PrY98mrtf+2esNl8lHcSMtm6zXL3K2emM8WSIEDV/9LUvkqUxg3zAYDDCpH3m0zmVtWAahjmcLR1OQBxLFqUhi6GXSq7vJGwKOLi6jVQOHWdcvvYim5HkEUt6kaPSQ1aPXsf1hwjjuJtJRmZFvW/ZSy9zIqb0lKA6XJMJSdkWpEkP6xp29WXEFc3J/BQZaVzZhLXz+8FP/hKGVooojVBdBz/rhcbsfDplNm+6IoEBF3ie4/GYtlsXn84XbDZlSBzNMqIoZmuQkseBH5RHGXGc0ljD6dksMPysQ3cqQq31RSDLe40Yiatb2qJid2sXc9OwLgveeOMNnBSgLCh4VMO333iDDz9/KzTO0oxENQG2rQVOCowyRE4H7qfoYp8BGUX4JCFJIrb8ZdazmrqsWRZr3LbCOkWrM3Tc4+DKMwCs12vK0+uhYNTPWCxmOGfoJTFnDw+ZHx9z+/p1rh5cZu/5j3FwcIDSAus91rVoESLD4zRsiu8fHvI7X/oW33zluzxatBjA6gjXNUo64kh3VEKTX2lBEgVodFnUwVHgADxKyYuGita621OGEJPzJm5d15S2xXcR8qZtOV0VKBlUwtZarOcDTu0LQ/jz+PBgQ3TeBbasCwBjgpj2T6g63w+Q/b6/Rwjqur54naA+CmFAn//85/k3v/PbvPnW64HjJjzT01MWp1MSYLy3xXQ6DWt/qXDGorMIpwNrtI0dcZwQKU3W7wOeuirwwlHWRVjvW2iNpSmLTihgyPoZ6yKo79u2Jev1SbRms1xj6wZhDQYBzmF0YPQ1wmAJxce6WZMkCUmiaNuI+SoUYavaUDQtUiX0BmPW6wLklLPpETvbe4xGA06OH/D23XcwwpOPh1y7fI2/8+u/HkIg+nmwNJ8nN8twRfrOFSCcRnS8QHm+3n8P7uF55Lq8cIQ8UdH+NMdPXHqUUoIE0y0smqYrllgT1Ddty3K5xDQtg8GA0Wh08cbPlR7ee7IsCw/U+TwkfiQJwRUVwGtt21zIv87ZOefFBbxEqZCU1R/keOuYzW2AlvkgqZJO02Ioypqm9QipSdIeUkrKco4UCq0FxjtM64jHfXr9Pmw2JEkKBNJ6noeY9HPLER1BPJyQc+9bWKgYY+kNhvR7o3AM2nBRNVoTkkFA6i6KWHRx3sYyHo6wBEq61AKd58SDXbZ6W0Q3oLx6i02xonEWIzUqGxBnOZcu7TLo5yRpzHy9ZDFf0zSwWS/YLOY0pkL3gg9SxxG2dSglLzrHUfSk+yA8F1Xrc/l1XdcXE8j5JAlceDF9N5EkScJoNEKp05/ahfmnj7CREn9KJ+fHpW2+o4ALL0nTlEE+oFjPwvmVKiiqaDDOgQgJZyBp2pr1fIpwnsX4iFY35EmfKOnz0ksv8ez1K4x7CccnDynKJWmeMRlusbe3x9alMYPBgEt7V9CqR9tYyrhmMNkhzXuhQ0OAb+rIo13N7GzGfHpGHMfkSY7UgqaqybRA6mBX0EJjtaIqW4whPIhEjBeuS78AJySr5Zxhf8T21h4f+fCzRFryeDplOT1jZ2eHJBkyX6/wUrFpFHUZNsNaKawL4M5+NqShulAbRFGE1gE0m6Y5/cEIbx1t1ZJnw4vOk/E+3K9xhG0rjAnqMS00jiCnVDpCqnCs+4OcuLPzOedQWodr0ARQ2V/PIs57WKj8uYKje7/eXxQBnv4MSapp2hqtE/J0gjWSFz75UZbNA4pqw7g3ojXVk4f4Uw/zP+GBPi/odBLwc0vMgzcdp4cABQiNMSXC1lgXGC9WRLR1gUgFRVURS8n9wyNOHzuYNzx8+Nv0X/o4+dYuUZzQpjnVpsDWFd457h7eJeligqflGtulbAjCA6bfGlIBarOmryGyDicMZRNgoN61RG0TCg2bmLuHJXU2Y3drxNsJ2BWM2xXDzNOkgrqN8brGC0cUKYw1xFIgtcQZB0oDjlhLTP3uRbPzjogNuoEr/Zh/+Qev8vc+9AXcpZ8L92FVc+PG9af/ReeZCpDgc1UV/lxbELqEFxJvFMZwAcGM4/iDl2l3ajBJmOfOPQFBPeoRkUDG4XhJoSCWIYucH0tX6L7VQqI9SBck07mOSaVGd11C44OM3wsw3uE6wKbzBu0jIp3T701I4gTnHFmuyfOEPBtRNke0TUueBug+hMU3Kigpz5NIgKAqUhGjyRjXlJTVhl5rMQoGvRgpBY01zE4e4duKs6MNV649w+/81m/yqY9/gjyLwDmyNKI/3kFKSX84pm5adJzQGwYI8Kao2N25RJrmLFehSVWXGwb9jOnpI6rlhr/5t34R1wq++MUv8st/+2+xmM6wtUAhKcsaqTobGuddU8s5cFl6h3emS33UyEgzOzslj0PYwmI2Z6IVTmukBOEszgYLeFUVXaCEw7YGvGN6CnLQokfQ1FWI8xWC1jR4c96x1linCJU7AAAgAElEQVQcVdV8wEVFERIhnccLRcCfSpwzaBGhpKSxFVoHhZj1EukihDAYb5FGBetnt4Y0uSCWCRu1oticMs4icAIjC6zwFJWgzVMWacHtxYTSzhHCY0zDIO+xWK7Yv/xpyrbi4f1XGatdyvkpYv8ZyuNXuXTr5zl562WiKEPoiLrcEDlHO39Eev3n0HGwOq/WS7TQrIo1b772Ct/9zjcQseDh0Wlns1GBu1e3VGVD3ku5e/8Bp9ER68Wc+a3rXLt8ha3pfSo27O4dIHo96rplU1a8/fYbfONrX+Pzv/orbOeWd177Ogcf/kWOf/hVcIL7h6+yf/NjzA7fJsskl4fXOJ1NWVct3izJ016wNqqIqm6pW4c3DucNrRUURpIribGWqvGsi4JLkzG1daAHpEmCWW84kCPeHnuqZokXEtUbIQcZFBt0C3+0eMAtl7BZFSSZohlA83jO6PYletkukc4Z9PvUK8u9+VsoJ/E2hVgS++wDVW8LGTr9YU8RuHVeCMq6oixCbHaepMg4xrsAey/XG+om2KishTQNTU2tI5I4IlL6XXxM2bkP6rq+SG09xyucYwneaw2jRJhDI6kY9ke4S6BOO7i68BgLUSwx2vPg5JTj0xmTLKcdjXCNwwiDcy3Oh5Q3ZByK6jIgIgDorImeYPVX3pLHGshJFBSbFU1rLpKbpFbEWU62txdwC5Ei7udgDdo7Zo8e473n2uUrXN29hMt6IQ3ROLSW6A6s73z4fW1refv+A1594w5vP3iMVYLGPgG/v+c5EwIlgtNCKUUra1r/J8WE5/sTa0OhxvsnqWDnbo/zcAzhO+SAA22CLct0ipgPdJzvTWx4vrmn1na+U/06/+TDPl0A+E/N4X/CekX4vMJ51uv1BdS/qiru3r3LRz/2cQajCW3VUtd1aNhbQ+0MtWkvEop7otcF4riL9Y6S6sIGLbUKNisTOGybsujSrFqmJ/NQqLYhwbeX5SwWC3q9HjpOeefeXZBP+HdCKBaLGW0ZCp9pmtIYg6o966pArUIwS+xcKBjisS5ca2VVkWQDVJYTRRGZ7xFnMf0sJ0kjkjTi8fExh4cPSNI0iEYGfU5mcyaTCWVdQZQRia553B1qF3zjocEMF+DisHJ6j/PwY+v1i/P6Hve/d39+Ts5PXMRJkiREiDUtR0dHTLZGXLm6jzARbdsGuWrb4p2jKkqG/QE6idl0RPeyLMl6OVqqC+vBecJDkiScg469dWRJRn84uCgenF98ZVmSJAlRDJvVOrx2WZFEMa011I25UPporRkNJ3gPdVmhrOfK7uVOrlWQqgSdJlS2ZawVOzu7OOc4PDzsJm7LYDBACMHZySn7+/vEcfg850UP6UOcXxQlTCbbCOeJs5T1ek3bNEhZ46KIREcM8hxnYbVaMZ2eorXm2o3rzNZzlusFUZayaVtWjSTp5SjhmTy/w0GeB38e0DiBdcHGclysOTyb4aqGsjKsFw2L5QzbNjgJkdKB3YPACc9ke+uiKrtcLlmtgjwyTzN6vVBQ2Gw22LYOIOru8txs1uR5TpqGJDDvBVXTkGUJUo4Zj8e88cbDP9dF+NMY71XhfNdN4SXOhw3duYdRKcVw0GO9SDGNoQZcTyN0SpQ6HAVOa6CiLje4RLJawms/+GP6vW36vTHrpmJr/yqJivgvfu3vBaihsygliKMA9LJYhJS0RrHeOFCW7e2Iuj/kzde+jbWe/b1LXL1+HVvNOdrMePvuIdN1zWR7iygdUZc1jx88JFGKJEmRKqfxEZu65cHRNABXpaSfpWidUlc17vQUJcaM9yZgarANn/joi1y9fIUvfuUr/OCH36VoC25+6GMsCkOa9jDWU64C22g4GKPEEzWdtRbpJJvFnHIl2ZQFt2/fpp/1WW6WuNYg4pjeeESUJKw3NZUJ3tU0TTGm7tJXhmFxYlRQ87WW4XjMYGtMNui/i8FxMaR4ypr016+QE3Vxy9DZTn+M3xOuxeji59BZLKzGNn02/kfsmsv863/2Jf7Of/1RynLOIOvRNKaTb4I1T6TY5zJ0LyzGdgwyUVNVMXESUZopwg+4/8oe77z+Ot6H+9q0DVkiWBXFBR9AS4caxiyO55jJPrc++RIHUvKFe7/FgDmq0rzxhX9PX8BrPih+Ll3ZY2d3SJam3Og9w2Be8mZxyEGWsjetkUhiQmymldB3Flt2aXetpcLwKPFEreOyg9npKWUEeQSf9Bmf5IB/fPcOL2xtc/lWQSMkp/M5n/n483zr66/TzzOqqsEq6I/7rIqCyvqudubBSTa1u7BxeCS66/e1SPpCMcTzjddf5/XfeIv/6f/4DGeVJUOzM0jede68t4DFuyePS9Gdx1jKkKiBIpIx2isKY0jTOKRjdPPrBzmkEMRKI5VEosBq2lrjMo/PDT4RtLHCJDIU9U0beqBaXCzOrAvAyFDs90RNSMyI45xIWKSA1pcI6XE4jOzR4GiABolFYnyfRBp6CSxOX8P3n2M2/j7j0QGxjqmbNa7NSaKKKEoAF3g5cUIiMxAWLyDJE9q2xBhBQoTOE9o2J+oNsG3z5DksJRnwX/2X/4B/83//X/z8Zz/D//Y//w988hMfg80hOtsHFXFw6zY6TjHOU5QVg60tWuPwreWZ2x/mpY9+jKOTU/YPruJ/+COGgwEIwWy54uHikGs3b/Ov/sU/5+Of+BS7B1foj4Zsj0aUjcG4kKknrMMSrHxKaKx3HW4o2IjKssSnGpIE08LO/gGHh4e89MKCptxgexKvPE6HyHThPLGUzBenrFYlhbFMy0M2K8tq3RA9HpFHDTJuWC7Ds9rVDW1RUfk1/cENbFshWAU10Ac1vMTbGI/rGigqNF6ExCGxXYqptRYlLEZKIh+KoaHbrJiMR9TegrfsXj7grdn3ULHiUpVS7vZZ1feZLgzEkuv5DQpmfER8BN1OMdkAYR2D/hbHy4y13PDgzS8j45TtyRaf+uSvcfDchxHlKVu7V9k8fIVmXVH1+/jBPu7skCjStDrDvP4y8Ut/A6MTpPW8def7fPcL/yd3773FaLJNU1aMBgFAui4WxGmEEKFJppEUyyUb4VjWK5bFGW/d+RHrds0nPvs3OTp8m8n+TerVggf3fsS90zOOW8X3/vj76M0hH7pxmeHwb2B7V/HaoOwbvPnKF9HpgEZd5/73v8vlnesIbxiNRhdzziBLaVrL8VlBkmiiqMG1jk3lKbxnkCi0h0dnBadnS67sLRmnnrh/jdGVK7Ra8QvyErMJTJspd6pDdFnRi3PaYsWLH/o4y7fvEs0XROSwaek9dwm3M2LRlMjWc7KZImlQrnvmmZDUVHt/sSb7IIaSoRhAt+E8PHxIWZZsNitca0ikppfl5IOMpjasik0oLrams3rokDSW54CA1tDrB8xDWxtWizVlXV3sZSIBx8vVheMA3p9l4kxQM6Rxxv7ufpgPvbxIoQrKc0fSS5mv1nzjW68gGsOtS/sUdUMvzsNfcy0yinDeomQEWiGEwnrR7R1cKK5EKblahPsu8mhX4ZanxFojfcPi8YaiaZBasZMM8bGkqRqaokApwezkiPXRGX00adpnKGMeG48FtNLILkHJeUfrPCeLFQ8OH/PvvvQVvvPmPRaFwQuNjFWYjy7Wel3B3gehQByF5N/tyRamaSg3BcK7wFGTYIztlMghqOTpZ63rwkvCMZdPNb8kMg4Fn6A8FSAU7n1UUn9ZQ/gfU9i48Ox25xt9KRDeXyipwnt/8v2fdV3hvUdpRZqE5OG6rrl//z5f/sof8s6dN/it3/4d7ty5w3S+ZFNWpHWNaR2rzZpRL73YR8ciNGeaqqaqWogTZBLWRMY5RNPSljWrzQrrQzEzkl0jprGUrSHWMdev3qAoW3qDKUnWo6qaCzFI0xiUgNoY2tqgu2ZY1dQUTUw2zDk+nbJcLvGuZTAYkejAJ6yqiqp21F5z48YtdBxzND2lrluc1qznc5yzPHv9Ki9//Wv84LUfEUWK7373O7z5zl2W65JbH7rNpybbjNKw55cdJoWugYULHNh3n8f34d78+PkW78+0DOHxf77xk4GNfVCnONNg8JyennJ6GgCmeZ5TFSXVesX+3iUipUNMd9sSMm58SJ2RMihy2iZU93o9NpsNs9nsXXHVeZ4H20jbXlxExlnquqbX6+GcY7UomM3OANjZ2cN7z3pVsFgsGQz6DAbD4KtXCoFkVczYbDZsTyw6jhkkCZPRGDHo4VQobGxW6zDZd8Czk6PjkGc/GNEf5qzX69DB6/cv7EV5nqOlpPQeVwbJJZaL7lLWy0MxxQuqzQbrBHGU0uhQBBlO+jz77LMUdUXlPVLEuMLQRA2lN8zrgqRcd2A9Hbpq3lEU68BEKRZUZYmpG1ZNJ5tPMnpRTOMsZRuKUWmesV6vWa8Df0ApRZIk6DhimPcxxrBarcA5kiTh4OCAchMk7GVRUxQFSZIymUwuKt5JEjamm83qr3Rv/V4FnHdbWZ4A0mQXMSm9YDScsBnN6KUZlSvwxqOTjJHOIW8p4hQ7Kjh89ICyaNmYFYt6RTNoWMxOuX/0iL1LV5FoxqMtBr2MyWjEZDxGSkiS0LkxTmAcSBExSBP6uyM2K4V1LZcu7bKzPUFLx/2HR0SJhmxMtVmwNJp6XrCcL3j84ARlIM0ieoMxy1ZS1pZl5SiaCowjiQT9NGFrkCE3NTPtubI/QitPXS2xZpvtyZif+fQn+P5rb/Dg/l22968iZEbVqdCklGxWBYuzu8QyJ+v3AEdjWpJ+TD8N1/72ZIsoiZmuZpimZTzaYWdvD6k1TWOIogQReVoc0suuYFsFy16kGQ63WcxP0UnKpYNr7OxeojcchQJPJz0898ReTIBB/PDXagjCw1c8/ef3eL4+uUb9+f+g8SucmEIz4ht/9Aq//Hc+SaR94IVFAuMMIjztUU95x+W5V9qfM3A8VQ1R1FCWS/rJPtNjxaOHD4Eg7W6bmjQNILinlXXWWtZ1icxyPv/rf5+bt2/xT//F/87B1SusTx6QrAxXtKY2hg9DYFg8PGVwPGU8HPJqM+XGh1/g0xtPb/cSj770XSQhatUThCCNjhgbybRt+E7iOfCCa7XB4JlrTc+lTFrBm7Hi93LPtHjE9k34JbHhsD/i3tGM3e0xd+/e57kPXWO6mrEuLCqJqV2DiBW6aZEQmDgEVachxFfjw+Za4bmsa9KuM2iyA+os4fd/819z4+O3SK/d4Atf+ML7nOenFlDdmahd251xi3QOY4LdzRQVEk+extQ/6QX1UxhCK3zs8a3DS4eKwSWOJgIXScgjnLDdhRoiYJEQd4vp1nTWAoIlLfISKSTSeWoXQgWsdIEz5C3eWlrvWDiDtQaMY5ydoM0OrBOG0YCe6pGUt7h89Xm01kxnh/T6ml46weM6K6+m14vAKZrGkuQZcRzjhSDv4lGFC/YcqyNkmga2VMd2E0Iw2cn4h//ov2F6dsrn//Nf58WXPh4SxXSMUBEqzpESIudABPt105b0hyNc0/C1r32Nv//3/wHfe/UHfOYzn+Gtt97m+OSE/f192sZwfHzMtVu3KYo1g0GPl19+ORzz7n1IoCnWyEh1926Ioy/LTQC6K0WWDpmMDuj3+yS+QkURH33pJR48fMS1q5exbdsVNsJmZzDpMz+bkiQZZ7P7PDo5o64bqqplb7LPg0ffZbSTIWWL7BI5itUptlmiZITyhjSJWNQN/gOdP4O9Djo2lPB4ZxAywmMCD8OFRbiTAmVNF/4Q1DfOt+T9Hvfv3eP6M7scnczoP39AqhKKk1OO+xuEdySDPtJpZs0JvzD8z3Bljc93GG9bTjdTHj54k/WypK8dW+MUh+Zjt59hb2eIW8/xpg5NPqHJRgNsvk2xPGXnU5/HF3cQWtPGO2zWFTL2HB3foyhX3HrxM8zmZ+T5mKOzKVp5IpEz3yxZlTVXLx9w9OgBq/WC8fAaw36fxWJBbSo2jePr33qFWbFmOB5y+fQhX/7y7/P48SkPD0944flnmJ4ecbD3PP3xhHde+SJXbr5Ib+9DiHvHLE+/SzV9wDM4nn3mBaI4FA89iiRW9PMe89WKS1sW/WHNmw8eMZhtgurCSHyrWDYWa4PJLYsVy+WSw6MptbUMJlukkyFcewZfrrAenplsUdUeIyFXBdOmwO/2kMbjVcu9P5qz//N7LAjJNg2GSCls3ekBLtTQ8mJ++aCGkop+mvP49Jij02OOjo9RWnB9/zK51nhj0d29kWUZOokpmxa3KciyDCHUhaoDBMM8vnAYrFcFTRPmRZXEODzz1eJPFG/ebxPnCE3qrJcT6YRLe5eJdBJsx7ZFZ7CzO6YXjTDrNW/fvUsxn3NpPOBTH38RWwgS7ZA+wjUG1evCEJ76HV4ohJA46TuMhKRpDMV8Trny9Pt9dvYu4XzDelUyn56FmHQ97oJPoFwtcbZFWUvPCLwLipcoSxmMhmFN4V3oo2Cpy4aj+YIvf/O7/PCNN/nd//hHnDUShEDpLjRbgvQOhOtUD+frHI+UgiSK2R6FJF1vDU1zRus9xjrSJDQYz0MEnu7xvUvV4nnSCBQByBwCM8IQf7Z6yE93dL9fXZwl/8SiA+DAyr84iF4IAdZRW0OWZQBsb2+zWCyYzmf84LXXmc4WjEYjer0cpQTDK/vMZjN6ieZxFJwyVoRCqCCsN3tJjNYKrANjabEU6w2PTo4omposiumnOV7A5d19kigiz/oMJ2OsD0E7UitWqxVSK95+/Q7Sw3A8ZjyekOc5RVVDJPBIatNStY6T2RIdOdIopB5WWiOIqWrLpjZYp1BxxqatefDgkNYYkjRlMB4wGPS49+A+L7zwAr/xG7/B63de5/LuVcbjLa5dvclgNGTcHxDpKCjDnUW4c4j5BXbyYrgnB/m9Dnxo6HR7l/dT0AO4v8AF+BOmU4WugjEtXkBZlqxWqwAAyzKiKCJOku5GDgUfY1ucj4ikwsoAZFWRRrnQlc2yLMDEOoDxOfzLGMN0Og1Wn25x1Bv0Lw6CtZYoihiNRhhj2Gw2pGkeiklVjbUBFBbriKLcIIUiiTRtokLn1CmkFghpgxxMh66xkU13QjzOBXBreF/thazbOXdRwDkvYEkZ/r33weCjO2mYF65bjIFQIf73adBzlMR4AVnWAyWharFKEWsB1iOcw3iDjgNA03vwbYtWEYlKKNsNpvH0sz4mgWK2YrlZh9QQJE48gam21rBaLHFdkebcSuWcw8TpE4mcEl10ekSrQxKR7NgO1oZCWkgeOrdWGeq6/cA7ztBN0NLj/RPmzfn/f79hOpCY6EDPcZQiYo0vgr0uVVGQweoEbxuMEug4xdcO14Rj4J2hdY7NqqIxhrr1DAdjRoMeVy9fwWNRkWTkc6QCGaU475BKgrA0dUtTryg2S/JeynCQIYyjNIbWS9AxKk4pa8u6WNPWBic0SSJJ4owkzYm8x3rLVq9Hsqlp2g3NeklRVwzSUDmv65LaBACdsR5ngp1ib2ePR2enbNY1s7MpN559AUTEer1hPl1QVy1ZkhPFiqoqaE2J8wrrDVhobMNgMGKz2dA2NlTEsxQdxzgEVVXTmoKkN0AjaeuKYhNg3kJIYhWR9nKKdUYUh4jy0WhCEmdYLy6MSGEDfm4cDvLGv15Y4zCenqAvLDd/hr+fxpKyraGcoCPHYvMIQ0aSJVRVhZTvk3LDkwXK+ZdA4/waJcDVGcXchcQwT4CySYno7vGLhI3u/Top2NrfZ+fyZU4WC7ySFE1LvTI8G0mk0AhjyRAYb8hkjG4bmM4oPaR5wlj0MQoi79FSEHdKYAfgBE4IvNYMfEvSetZIEhRjG7MQlsfKco+aZ/oTrqUtEQ39awPUWcPOZMiybCnXJXu7mptXr1AUb3RRzglVW5MKge3snw7wzoYulpedtcsHWouEfqa4NMgZRQK3mfGD7/6Ij/7Mc6wWS370+v33Pmniqcf3+flGPbFViWCtki0kkUJK0OKDNAw89faUxKtgV3PCgmix0iJE1LGag03MEe4tKUNMp+zYTsaERJTwWuH5KH2wBlkRFh0Gi8NR+RZn2q5VI0hERCIdUW0ZJpdJ1Yjr1z7EeHjAbu8KZbnkcD5lOOyRJZ3qyYdu6XkS2Pl1eV70kFIh4wTRiq6TH3VK3jgcd6WQOnxfN4bdSwcMh0OGwyFxb4QxDhHFSKXxSgd7mfQIbbrNgcNaj1KaW7du8fDhffYP9vB1TVVV7O7ucvfBw6D2SUN3sigKYq2ZVkVnPY6DfJoQMd86Gyx4rg0WbG84X3JFUYIxUNeWLJWdNb3HVr/ParMmSvRFIVh4hzFhDRLF4TOWZXGx/tje3uXOI0Pb1eRMXZPGgRuYJgl168F5tI54vxSYv9ThJedMOu8Nwuswj/uui0uAsaqLOS0ocM7v48bULE7nyEuXER0PoecUPo6RIiZVmrpLgTuIr+NkRhU1jIdjjh9/n8XijEuXL1G5M3xRM8ohynI+/bO/gs7GqK0r1G98C6UCt6x1CrOZo2hYPfoeWRKzc+MmswIo51i9xWY6p10tKDcLdq+8wN7eVfarJWenj8myAfk0xFNjGvYv7TLOY5559hazszm3P/QC33v1FbCOdbHh5a++zPbeNi9/+WXmdcV6sWQ+XTMZ5gx7MVpG9PIhz/38h3n1a/+WBz/8Frd+7tdQNCzWS4xbcXZ0yLMf/QzazoijHqfHRygUZVkz6mlsE/OxZ27QFobj2YpVHdYvICmtomcdsQ3rKN8VW9qmIrU59WJKfzQi0xnfPnuDVbVESk1mAJ2gBrB56xGD7ZTepYxhMsZKz9IscbjOZvru7rX/ABU450MKAcayWa1YTGcoCbvbOwyHQ/pKY5sWWzXBAg4XlhxrbYcgkJ16NtDnHMGGxvnzVCukE3hvaK2lbp/sGaSUf+r6OE4S0jTHe+j1ejRNSL4TQBZFbI+GpC6laGpMVTJfLXl4csKtzQYRaWILonWISCBc6O4E+Gqwdl/YvZEIHaHzHOIYVVZYV9FWNbYsw7zhA6i69pDrkMjYbDaosiYWnkhK1k1L29bIWGOSYFHzQTKMx2LbYM85nc75/pt3+OFbd1g2gJSIDszrnQP3tBLnx86ZlCgl6OcBhTGfzjhnAZ7vP5QKITpBWfzem2QRSM8g6Axl57bo7uf8FfQGn7ocLgpu3QcTHozoPudP4VedJyif76X3Lu2wv7/PW2+9Rj/Lqer2AnwsJfQHAwCSPEPFgS3klSfU2nyIchcK6UMT0CmBRFzcLwCbdYmtLWmUonclWsVdGnOL7M73arPGesf161fJkmDTb+uw54+ShKIKjLfaGs5Wi6BKdxJhBU17rrRSIGtaK3FWoHSCEIpyU7Epyou1RD/LyfM+s9kZjx494vrNG/zqr/4q494WSkUc7F8NCXUEtd75fa084ARSPAEZ/1lGwCr8p4s3P43xE4KNwySgugSLsiyZTResVyXD4RClFOPtHcrV/ELG1rQNdV1feFGzKCZJ0hBpttlcfK3X6wv+Tb8fVCFHR0c0TXPRZYMn6hFjQgc0SXrEsacoClarFb1exq1bz17Is1brBYtV4O70BimTfkyqFVIrZCQQeYqKwXpBHCuG+R6NaTg+PqauKrYmY4SEpmlYLpeMx2OSJKGsG/KsjxQea7uYVgFxrBFOBBijztBeUTcFUiiiLsI704ppsWKxWVOZmkv7O/QGORQSdIi/i6VDOhMUTdUGWSkiAocgjlJM41jNNzQlOBtTNBYnHCrJybdiIhcWdEVR0Zg2dClbz3g8vihARVFQiZwXZ859u+V6g3EWiej8v8HLOBiEgtlyuWRnZw+tJSdn8y4itXkXPPSDGgEw+mSj9H7+w/Df8L18StaWDQak/QFVGRRgvSRGx4osyqiKkiQNUbJXr0Y8nk6Znc1Yzo/ZRGviKGU4GoJ2NK7k6GTK6TRhVU5p7TMMBj2Wa8l8tmY43mJnsk0/H6DzlMXsjLt3XmM2PebFD38IpSz379xBuoiy9VgaeukQITRt5RB5y861EXkHC4/jnMGmxhORbm3hnaVYbnjw4A7VpqA42zC4MmIwmZAPRxDHEKf4LCdKe2wNJnxUp0znS0oTmE7etzhj2RlNkJOYSCY0puXu43eoipI8S5jNS5wPXJztvZbd3V3SJEcnKV5IzmbzIOMlFC1DwTBjOBoQdQXT1WpNa8N5y0ZD4n6P7dGI8c4u/X4fpQVSRl0t1Xb1m0BA4j0AYn/VwxN8zU8v0d4rQOuc6fU022Y+24BNSeOIg5sjtvd2OH3nmLqsaBpHnvUwP97Nc47zHpuzFtstYLSOKQtDLMc8fB0e3V+D89RVSV3XaAnL+QwVPZFpny8u54slz33il1jU4XVu3LjB22+8yczBTa1piwonAsxcEtgnmVZEShAJy9HZlGE/pT8O14F3YUPvCeuS3CsWwlC3hpfIcELQoimk4/dZU2qIgV+OoCjPuHZrm4m3XBZbHLwY8/Xv30UpAfsTTg7fYTIa8tzlS5ytG9Zlw7wsSbvflUhBi6D0wa3sscR4xhIiATrpkYoNA1Hx8zevsDvY45tvb/in//wP+L3v3eVHJ+/9wH1643H+c+VdV8SRKO9QXoDSJEmEkoKIFtn8+a+tP8/wAlSiaNoWK0KSR+IdOs/IMwEyQPbL6FzhJUPXScCT5Wwo3CmliGSKUOATT+k9MpYgA7zSGIdpDbLVSCkYp0N6PkHi6as9BoPnEDrBzBsOH73Dj+b/gUoOeOFjP0uSbVNuJIOBQuvw7Hc+bCCdN6G4pELpTaokvB+paYUC2yJ8FBol3Vxu3fn6JGJZ1EgZs3/jNsvNBpUG9ozzgijJLhZ2iVcoAa2pcUISacXNZ5/h61/9Gv1+H28dn/vc5/jOK6+ws7PD6ckZSaw5PT7COMtqsaDYrDgvKtV1jRKCfhrTWg8iNKZaWzMcDlgtK5Q0WNvy4ME7AExyzYu3b3B8csbW6Bpt09LUVSRM/XEAACAASURBVOh468CRiVIB2lO1ITrVS4OQDXt7l9gZD3DOc3QyYyQhIiaOB9S6xFQG60sWyxIdO2hihPsg59AOri3O/6TCefUAocEUyehJMbkrvMqnbj9rLbc/dZOHDw+5VjYYa4mU4GTkaGRD6z25A+Nj8v4Bp/4BW1HM7OgBtV+wfWMX7Ry+jbFCcrB7i62r1xleuYac30OdLNi4BctFQzKaMLn8DE4O6F+6QhZJVJKzaWt8fUI8ukRZGjIfs/fsx7j53Md5643XSdMcrSXzs8f88Vf/H3r9bXa2e7z+w1e4cnCVyY0D8v6AZ198Eesa6vWS8dYuw91t2uKUN370Go8XJT0T83B5ilCSo8NTfumTNxlvTZBJhIwzbv/iPyKixCxOuP2pzzE7OWbx+B1cs+DuD19m58oLKL/h5GzK5b09xoMetClZOmS92fDSbUvdVny6qHnt3jHzynC2Kmmdom49lQ2m3wiJ1hLvWkaDMbNyTqUs+yqmcTCvF7gkcNCqpsamgvvfe4vnfuXTHLUtWmkMYc703uGduJA7OG86ppj9QDfOUgjyOEYj2BpPOLiyz2AwwJQFgygl6kO9LphhMK2lLMsLRofsGrRNVwyQUnJ6GnAIeZ6ztbXDcl3gTMt6U1JUFbU1F0ycPy1JyADD7W12D/aJ05xrV29QrNYI48i0YDIYcHlvl1EZs0CwiUKC0Fe++U3iXsKHbj3Li/EzZCoiUzG18whpLxiewYsQlDgBrK4osx44T+o1cr2mOJ1x5427xFozzDNSILKScjNFeEidQFpDXZSsy5Lp9IRGwe7zN6m3+mglUDIUjJuyZDqdcnQ25f/7yn/kP/zBV3i8WCPTHGEbQpiDCdbfJH4qjl0+UQoKi+gUyOPhkCSJOH78OGDqgCiL6GVhfV528eFPW9dCAShY87V3tCYopX68yXaunP7gy4pBIWTPG5Pnexfv8d0EKDx/IeWkF09sW0pKNkVBpDUCxac//WnOzo7YVDU+jnEWcIYkSfjsz36aawf7RFHE0fExt595lmWxwZyLFySYqsYJH5KifWgaYANez3rFnXfepFoUOCf5zje+zfZki/HWNuuioDEGEWkcDh1J+nmPRMahmdK0na0qMKyQApqauE7xQmBMhXCwXK4xRUkUJ2idsmodtYFsuEfWH7B6cI+2tQwGvaBAEg6pHFGS8q/+5b9ApTEf/+QnuH3zQ9x+9jZt48iyHuPtHeJYY7E4R1DnOR/WQN535+vdJ+X9CjQ/bn97v3AWL0WQrP85xk/MxDmPa3OOIMUrik6poXGipVqHaOsoikjjmGK9Ct36tCZKE9RwyGaxpqg6q5WgAxmJ8DodIydSijSOOTk5QQjB1tYWWmuGwyB3WqxCEWm+CJXZfr/fqXosh48eYI0H4fDesr29TZrGGBdiyFofTkisI2QcE0fpBfjJCkNjSzbFCiklu1t7PDw8JEsNVyZXaZqGum3Aa3we6OayK2pJHSLwQIBpEUqgpKR1FlcbIifQPsL7cPzmywVN03Dz+rVQafaaRHt0rMgjQdQtoldNxcnplLZpUKVHywgVaUSqQGj6vS2cbSlNgfYF1gWgbB5l9AZ9VFURRdHFhi2OY5qmuQBLpXlGWVUsZnPatiVSkiSKUYILqLSS+gm8TeqL14uihChKKIoGZz/YIs675JLvcQ/9yZtG/NjPBSrqk+TbGOuxPnRIkygmjRNsa5Ddhiztb1PYCOcTBBG9KEFHEoGksY5xP2V70idJMvZ290G0bFZzpnXDelFSbgKPqclmTIXlwd3XeXT4gO3hmIMrV6lNwezsMfKxJlWKpnUURcVwPOb6pX1sW+PLhn6akSUpmphm7EmSjN29K3jhOT4+ZhLHrOcz7jy4hyFDJQP6kwP29i+TJAOibELrBZFIuH7jEnsHNQ8Pj6g3a9I0p5el4AMQsSkdZVOzLtfgHK3T7O3t0LYBav7w3n2cg52dHbLeEOsdWidkeT9AXaPQZXfWcv/eO9TlmjhOGE0moRDrLN4plJK0ArwTSKFBRMAT39T51CZ8R+z/yaetv9zhCZLS8z96j1fvMVG7J+lG51GjWo5w1Tb9rRlfePmL/N1f/28x9SHG12gdB7h1ml3ww568TifRdDbAS52jbJdomVGvMo4fNpSrmroqcc7Q1iW+S6VrrbtINzhX1u1NtumlPaSISZKIDz93my9/+atkk302xQl5Chvj6RsVEiU8VA5K03KmYLHcMN/OQEDtgwXHdQYkC5zIlj2n2SLm5WRDW8MsUeRCMzYR21pipePNzPOzOyMONp7n9nNme2u2jeTyICWLJPPDOfuTPomOOZvNSaMe8/Waz7z0EQ7vP2S9XlM5h1aK2jhSCThPT8AkksQKBnLDtUsTLm+NuT9reDQtGG3F/Ns3F3xvKlnXJ38qcPN8Xsm8CmoxQKlQZGh0xNl6jhZwfW8LUf7FL7GfZAgh0WmCaRu8kEgliDxMhhnj3iD4x5tgi/LeoeS5+NRj1JPC90XzxGpaLM55GmtJ8GFLoBVCalIHA5+hZEQieuhCYMuGqj2kKo6Iep7Kz8DG7Ma/ykc+94vMm5bHj4/4yO3nsU5dqG+Df1/ipUAnMXiJcQ4VpUjTILpggLYJ6G6HQKoni/VzObrrrA+bqkFrTRQlWMKGIJKOwkgErnvt8HeNUBRlzWc/+1le/fa3aZqGX/j5z/E7v/PvuH7jBstNcfEMLJcLzt6cUdYFVVWR5imbqqY3GBMpRb1Z4TrGgdaa2lRcvXqZN16/h/z/mXuvJsuy8zzzWW6749ObqqzqalPtCKABAiCDTkNJVIzEIe8UwRijiImYi/kHczWX8xfmahSSbjQRE2MkcURJpEA7ogNJgEA3urvalK9Ke/z2a625WDuzqoEGSVBEE/umXGZW5tnr7LW+73vf5xWKR4/v8uj0LkmSMLz9Y9z58CNuv3iDOx98wO7ODmmqaJoqyEWRnD494eOP7zHsbXF6cc5suWAwGXB9f4eocezsbCPTc2LhkEJTV57ZwnF47VWenn1IPhM0jWMQDZCcf5arMcQPXB5QZdQp2iz2UnnjqsBBwyC86BRiXc0v2gBFrg3ROEI8KQGHrhsuspbUO5zyKHZJXY3Nz0nlgMJVjLdv0reaZl0QXd9k40iDjkD3GRy+yvrbv0qtMuI0pT/coBYFrdOYrZfpyTmieowVQ6QsefrhQ8Y7u6xmj9h79ctsbQ2xbWBApaMRi9OHnExPGW2mHNy8ifJQrnJuv/Iiw9EGAIPBkK3dDYp8wWtvvU5vdMDm0QH1bMqPfeVnsbXg7e+8wxt37/L1P/h9IuUZppqtjU1UOuLsfMHOdsZosEGhGi7OZhhaXvu5X2H66APK03cp1xcYIfjiW18hnz2mKC2trUm1xPQTjNymLEuW65ytUQbecXyx4INHZ6Tas9FPEL6lLUuq5TSoZfMZwheUbQsm5ma2i05SpuWakiXTecmTsoHGUVg4P33CZHMD759Fal+m+QVhbWDMOMun2xB+aCsRDvb2WeRrat+gI3P1dymCOi+wq1CX1FXYK6MoIvNQr9ZhgLlcgZJobUiUYzjoM+wFi9x0GVQFoUhtQIormHtI5HHft9gziSbr90n7PRarJeW6pGnCHt3PUsb9HqNej/0oZaOf8nR6TlFXnF6c8vVvfJPFYsG1zS22sh6+tIjMgQulZrA2dXuWF7S2DYq9pIe3DjWI6CdDtI9ZNSe4pub06TmyU7oUUU1bNySVI2ugKmouZlO2jg6Rwwy9u02VRsRlE9g7Tc30/Iw7H3zI2++9z7/51X/LtPTIOKaqLciQ0Ku7M1JdBfXHlfnrclAn7JVNSkqITdQxOAm1iTFsb2+TpikXF4GTYq2lKMorlc7lualnItZlgW8dTTew8N0RU7i/PYf+ZQ3jBFdx6d/tnxL+meXK+8Cl8c/9fM83CAXBKXIZq6G6r+e8Q0mJURHGhJrurc9/hRvXXuSf/bN/yp27dxAKvvoTX+aVl14ici3f+KOvc/uFF7j/wQeslgVIT9FYUBqpIxoVUZcVyje01qO8w7UhlW91MWVrtMng2s2uySN58vgxf/Jnf8ajJ48p1jlxmpD2e1gXBklZliGEoi7KkEQtFSqJiLUhShNaZEhnVZ7MSPTuEJ30kZEKEPI8cGGPXpyQ5wvyxQWRgVgJDq/tM+yPGPeHnDw859bN26xWK7RPeeW1z7Ozd0DUH2KSFLSmbgOY3GloUUjlsZ1aKpBpLSCfxYyL7x0wh4bPp7hCvh8/R333jf+rraEfuBqy1nY2hyA1vJRPXUr0a+fRQqC7eLdAbg/NCOWgLkrqtr5SfzwvNzTGBJipBdVBFi872UVRXHnez6Lo6s3tnKOpW0qRE6VJ5//ztLZEicDU0WONNjGuKpAoGtESKYPRKVoloEE6iaCTT7YhqciL8IbAe9brNXmeY2KDdxBFhqYtEDqjbGpsbSltidIgbGjuNE1Qp9R1jWtq0kySmIiqA4oKZZAmFMr9NLuStSspERqqskVai9QKJ4P8fV2uwvehI7LNMQEE3RBJQesqdALCaLwMRW/gi3hcd4Ct6/pKgdO2bQBWaYMVnaWtqiGNiI1ARQFICHTS3KK7T13n3MurpKLL+/+3cf1F9pW/sEPqJRZBnKQMxhPsNL9K3UIG2WEAa4ESgt5giJcK17asp1P8urPsJSlGRSF2PurhG8siXwIw6A3ZmAQbYLnOWS+WtDZnuThDS8HO0Q1M0qddtaTDMYPBgqaxeFdTlHPEXLK7sYOJDGVd08sGbG1sEqmE2WKJkQZ8S1GEBIU0SjEbiv5ixaxYM1/VNE4xnOyQZX28DRHg6IgoTomTHk0jmE7nKKUYZD3WjcW3HiTEqWFvZ5OLiyllWdK2WZis2fZq6uGcI41SatEGxZlRKBOjlA7d+taihcZrQxxHxEkSmpBCEcU2cBG654H34CxX0lzvg/3lslD+EUTi4Ls0nksZqe6e5Z/cXBVCKpyzOB/k2dY2OGV5On3KtZ3X+R/+m/+CUiTU8j8hhKShxWRRiCH34LyCLn3P0nbSZY9vA1eiFVOS+i2KBVSr98CG9KaL2RIvPA2See3JdGhQJj1F1TSsbCiOdWqIE0Xb1iSDITu7u/ReHfHH/+53+aVJjzejhq8fFwzTEYXLKXy43y81grnyzBOFebTiFMdupJG1o6RFxim589yRjtO2ZBLF9JTHuIDA1RqiJEIKh64tsS2IxzHRwYTk6ZRkK+bLtybcfXzGYh3xyGWU5YrhKCHPa27fPOS9t99m72iPQeq5/3iOwNEXhoVtMEKjlcaqklrCYBhxY2h4eSdFNHMGvYy7y5ZMR1xPoFgIqkulEw4hQ8qGdND4CZAT+QrcmLV09PwCJWHRejZSQ2NXDGMY9RP6sfhbeS4WWEhEt294+gYmg5REG6zwVConzhtWbYEfGURVI7xH6xYLNK7GuBScoBECh8J6wlpJU8CTWtBo0igl8QbpJab1WNvSuBa9NPhezsq1lGwzyo4Y3X6Z/+v3/jnj4R67o5+gQYcDkvWoKDR9myawjWSXVAkO7yzKJHjv0BG0tsHaFq3iK7bTswhPhVGXSYruaoImZRhAWSuJCLyavFrQTzdwsaItGnAKkWxwdOtlTp7c5//7wz/n6LUX+c53vs2Pvfg53n38MRfCUdSe1mYUVYWSBzTNAqMStPQMBhllXZMpRb5ck2SO1lmOL3KcLekNDPlsxWg0op8ZlosFyX7Cg/MH3BjvMj9fsLsxII5TbAa2khwc3uTtb/0+iR4zc2egNlDRfdb5DtvXhyT3BnixptQzfB1RTM+RssXYhJfSr/C0uODx6Tc5OvxZtP0+dsEfwiUI6WaXKragvnheHQtChHvlvb2s8nG+U7l5F5RiLkz4fWKgdldf01rwvqSIS5bNktd5E68E/WwMKMgyYh+RT5chdbGeMn7z81zc+WO8EwihWM2W5PkKGQ/Qgz7Z5g5iuiYa7jNfzvj47n0Gg1FIyRlvUZc5SRQjZACnR7EiSmL29w+ZXzxme/8AW81IM40QA3Q27M6lICNDT45JsopeL6ic9XCEay0m6/PyG1/AS/ja136NSdanHxlaW9LMWnYOb3A6nbEq+xzuXWNUFpg0If/oPzHYexWVpxxMNvC0rFeLLsltyPGTxxSzKSpSuNYTScUgjTnPV1hrGSXw4uEIX7fUTUGkh0RGkUhJu5yRbGySD1J8taRwOc5a+sQ8WT8ld5Yyn5NrR9xPcTgibbqpskRYi+hUe94/P+joFKyfoQVfSMi04+ZkiLOe5WwJeOLI09uYYFTGdFGRT+eUTR0YkrMFVWVpW2haR103KBOjlQRjsNIwK2vmqxV1U3f2C0fcNQ/aDhFxebaBT2fiVE3LMDH0BGS2wlYLtGpxusVE4Ww5VBlJpDBWsN0mVJVAlhHVfMrHdxrev/0S/e3QMNxuDHgQukXolLYNgx8hPMrogJTobOpeelrVIvqaZLtHlUPdzPFVFQrWlSOJDD6LuD9bUccWc3OPdmNImqb0VERUOGKmeJ9Q4rm7LPjdj455++MzHhTQSIWtapSSV9ISZy/vS2jchrrSQfesMAps1WKFpF7WRNmETGUoD85BqiWbNAx0gowVIo4o6ppl95pa16J8QFvMakfVtHgr0EKh2mdnymfXZ8to+u7ryqrTKc//oo8JFnnfMX7E1d5nhUdb+z2fLUU4IyolQ32rIpTQbG8c8Iv/8Jd5cvEQIxxHLx2RJZpf+7//NXWVk2UZW6NtcAIdG5QJtt3WQ1s7msYRKWi8pG0s0jfEWjHsD0L672BAlsZEcY/RxgSlI5CCIl+xWlbUeUPZWpqiZJXWKC1oOjZr7W1Ii5MSlCTNBmgd0cti2s4a7KSiaGu0CSgMgI3xmNnyHCFbUiNJM0MSGXpphsQQJQmHu0eUo5zXb73G7v41dNon7g2I4z5ePkshDa4NG5oxn3hV5SfuR3iOfZeWy//FycnPX0L+9SubH7iJ09gQiy20IomyqyaL7ywW/SRFdC9mUeZsbWzi2GCxWFBVFavpGisExmj6/VDcXtp7AjCMUEQLHZQ3/T5lWbLOc7TWVxLHXtIDKdg9OAgKE2tJ4hghPOv1ElunZL0kNHosrC5m5GVDmiVoadA+JTI90mQIrsIpkMrRNgFqtrW1RVNWNE3FeDykXIc49GG/j5MCEyU4gqwyLwPMWGhJWeaIViBdUBQ5ERgz6+WaaduQbfRpqiIke0022diaIISgrOrgW8fgnWJRl+SzgqYu6W/3IZHIWJAlPYqzgrUtqJ3DqAhvPaJSVG1JmhgiLUPUmwt07TgVlGVJ4581zy5jxotiHbrxJvCJfJIgvKdtQ1On18+ITIzMIhpbI2WAXLVNSBIByLKM/rD3t9bE+X6NmqtIegiqrMt48UtbChZnNYPJDl/96s/wW1/7f5jPp9Rti28lm5MJSRJRtQ1F3jDZ2WNkLUYZHsxXVE3FOu8mrlVLW2tKV3PSHLO9exggwS5jf+8aZVlTFQvSOMZZh9nYJdlN2N87Iq9WOKvYOvDEcY/5dMbHd++RrC22XXIxPSZJEnrZCNWPITFkwzGNCh7W0/k577z/YUh+6/eJ0oTCKk7Pc9JswHj7OnvXXkbICNcYrO0i4xNJLDUvvThmNl8ym83IqxwtNGaYsbE9oChLStb483OKcsXZmWXYD5v3/sER/ckoFBvSIaRBmwgnVThktx3VXcYMxiOaQoaJkJJIqdHGEInQlJBGggzrNpUCCGoVIR2gQ+MIG5Q6P2KXROJbEX6VQdGFbcIh4YrVFJR+QbqrgmS2FUxn4NM+f/7k6/xYdpc7H94LTdGrw+3zxulLJc9zoDTrruxUgj6rZUWx9vR7Gpt5lg9mTPoZQnhOz+YMkhhPjVGK2XSOk4YXX/k8/Y0xJkooyzDptm3DP/nHv8Ljk8fsDjZI4yUurrH/+3/kyXrOvkrZ9mHKeG0n4u18xr07M6bRJts3domdJBUaa2tKHIOyomwbttC4NrRIhkkoooR1JNIRKcHQaK5tDrm+FWH8ktsvbDJdrXC24Ohwiwsylsc5/eEGq1WOGWjqes2X37rFO3c+pvWevb0hy7xhuSjYFhHCNwxkyyTSRFpjm5pbWyn7Q03JDvOiIjct3773PgtSmgik7RgGXgSbqAcjCRYZGr7y1S3+8A/nKCI2+obJxohZLvFFjhEJWQyGkLT4w/JCf7/rci+2NhQSk16P/dGA7f6EmAjrLYKGdV7QSkVetZhONVrZnKpuaT00XmDbmtbGqDbsZ61oWTaBsyRURKYSdBdj2zjHqlqyZk0ta2bZgqqsECrCyyfYi3f57X/3/3L7xlfA7HBwOGG9fEySbn3iNbpssAsRVHpCXK777t9EsFnhBVIb/CWHKuR6h9j07pIyMNvC+yWoAVQ38bw8T0zGdEqeGtEx7L704z/Jg493+Q+//q9JB5v84j/6u/zqv/n3DHd3GPo+1yctH95dsnnU8M43zqCM2b2x5vy4xTJFRoKq7DEYjJgv5jjhWawXDIaToMTFEqcaqRRttWB5vkZPesyN4OyiZJjB1mSD48fnvPLaFykrxefe+BJFldAbKOYnU3S9wzJ6nyh5leFoivUFT+8tcG3D/sEuT88eMzt/j5d39jmef4umHfLR8o/Ixr3Pbi1e3s8umQ/hEb7FC/nsnvuO+yVCwe8IgydPsLAIdZlUBfUk5Vq2gZ9ZMI5aeJQx9FrBzZ2f4NSu2E9fIpY95PSM6mLBYnpKvVhQeMPw4Evc/d1/QdYL4QxxJKnagiYvSUYvsvO5X6CYPuLp3JGsV1zfGvPKq0OWswtsuaBZLZFRSpTs4b1jtVwxGvRQfovZ/JzxZEJV7aDZRnpJ3ayQJg1TXVsFeO1wQlNPAyupzomTPrVtyRdnRHHGCy/c4nMv36RcnNFPFREeEwuaNqc8OWb/lZus5o616rGzmVL3+izPHxFFGzyZHtMWc6IkBZOyvH+HjcmIwzc/T2Uds5MTqmLF6nSBVgJjEgbxmKErOL9YMEpSxumAwbBH4yyxitBNzp6Y4MZbNE3Fffcxjxf3mbclOkrxRYtVKaPNAA9GCuqqDWeuzjKiBLhu37IdmDzAAT67Z6NWgu2tjFiOgkLj+janp+eczxcU5znKGEbbPaJVynpa8ujxU06ns4AYaFqkVqio434ag/eWJ0+eXO3FTdNgLRgjr5iTZdVenYsv1SGfdhkj6fWCNSjPcxaLGWWVh+cSEKmIvZ1dBh2f06huQK0Ep6fHrBZzfutrv8nmxoSjo6OQDOQVTjVo0wbGFK7j43R8k+7Vl8oEaH3qiZQk7feJ4xiqEO/smvAz5FXN5kGPLO0T9VKE0VcIDC+gbjSL1Zqz2ZLvvHuHr//ZN/jo/gMa74J7w/rObvXp9+e7G3re0w3wPNP57IojmqYJbVFS13Vwc3jfvd6rrlkf9ggTKYzRAW/SOISXV0Npd5UI8awY/wz7iX9jl3Pu6qwYuDqfvr6C2d8/Y0p6j2tqpJO89spt2vcrGtfw7//tr/HRRx/xrW9+nV/++7+IUoq9g30ePvgY13pik1CVOXnrkEYHvqtXmCxBmxhbeaBge2eTrc2dwIvtvqdhf8Dh4SH5MqfMC05OTjifTlmuC1arPADfq4rVes1ksonp0n2dc3jpA2AYzc7uNlmWYCLHbBZQKaOJoagrjEmJ0xTWAqMMVilevv0avf4YbWIWyyXXb9wIEiVtOHrxBuPxGJ2kZFkW/k8bHDuXopEf9esHY+Jw2QEEIxWmy4i/bOQgwsGobWtca2kbi1CflBhZa7FCYRRXYGOl1NWDzntP0TV1LsF2jbVXEaKX6hshBEpHDMZD4jimyoswzYgUrmmpqZFCEycJ6/W6A451cY8mIk0iYh2jhcZqh5YWZyXSBBWAE6BNeKBGUoNXlHXFssivos5T3SOKItqyQimB1FDnDmc93onwcd6RiphcReTrijIryauS+XzO/qTPZNBH2Ja2dUhEeJgKj20dVR0i8yRhQTrfIKOI0WRI2dm6BGCioC/UMlicJAIjFXndoCKFUDFlmVMVFVKHKaXrrGVlGWSbWRLA1KaDhDU+QKNCupcBKVB0Ua4iwrkQ4a4QSBXRz3o/8gv+sjgIJ8tuvYkw6RttbJIkGYv1El1V4X5IiRcGIQVaSawNoKFeb8DW1hZVVTGfnuLbhsYFe19Zt9Rtw+6+RGvNYjGj3x8ihSKJFVl/gG0l2gjSpI+IDYaEhDANiUyCiga8c+ceTSuJkx5F1bCsa5AxaVkh5RqpU6wIneh1Y3l8fExVWlgsiLVhWdVsbO6zd3iDvb0b6HgYUtO8xES6m1g35G1N0UisV7ROsi5K+r0xukuSc3i800RJjx4KbxtMHJFlGePtCVnWB6FwXiCcIFIS37S0QUfY+XHrACN1La1tEVqihMQKj3cKoSRRGmF03DUXn4tV9PJZg/r53/+IXbYNsZfB42y5jLV81oPp/tzJyW1rQ5PXN5wtTvnlf/QF3v1Pv05EgpXPNuXwqaGZECTAl1asrhH5HAweGVOuLUVeI5VDdNGI/dQgFJy4Bo1BRYL1smSytU3Wn7Czc4ToaVQUo1RorOAVT548YdBLyS/mTPUFX/zKa1z//MvceecxT2YFXiYogkVp79pNTldzvOwz3BtwY3uPvY0NHl2ccu/JQ5qzKTa3RNbTNOHViJVESoH1AiM9qZL0DSTUDNMew4FEu5osTejFEcuiYm9zwHvHyzD1EZbEaNqqpMlnHOxucHo+Zblag9JEEGS6iWQysGQiFP6DWLEzTDG0zK3AqZiD/U0i/YhimiNasKJ9dt+654bsGA4Sx9b2COfPwDv6qaGfGBZ5iRSONO3Ti0AKi1AgxOqzWoZhTRAapSEiUzFIM7I4ITIG7UIkOt6C9mgXImW9EHgtaes6MC66fShEh+gOrGtxBhpa6yUL9QAAIABJREFU8J7GSlrRUAtB48N6zpuClV/TUDFVSxoExiskLZaKdFMiI8N8XTEajbDV7EoR+ok1z7OC57IZ72yYHDsbmsCis0E9Dw78hO3wu4GClw0gKcPQRYgrC5cQgrbOWeYF9+/fpVqsGG/scPuVz/F7f/CbAdqetZTmKbK4yc52nw/unpJmEi3BKcvP/4PX+Zf/2wOWU8Erb+5x94NHtD7CeYETkqYq2NjYYrWwDIYR/VGC0RF1tWZa1kTCss5gsbxgsdynKQpSI1muzpFmj8Qk3L3/hDSLODyY8HjxEKnh6em7HD9+ysZmD1FJnLVMz84BSRQnjMebrNwhT+yaGoGOPjsmztXj2ndNG2HBKRD+qhn9PP/meVWt7xLlrhhiraN1QXVuVcN6uWBjYwfjNb1sA+slG2aHTTGC5ZLq/AnF6T1WqxX1omT3tZ+jKB5x/cYRIupj0j4ur8jrnLrfMHnzJ7Ct5fHJkje+9OOYKKEqZthiiWgeIrMdZLMi0bsUVY7RCus9Bt/Z/zWNiBgMJwhbgTREPkXQIQjqEuuqYGmXMXTFb3h+twgpufvBn3P3g484OtjDDhVvvvoKbV3ifM1AbGK3dnjw8SOuX9vAL1o+ePSAF954E+0SzPlDbr7198hPPqA8uYvopWwOXqeq5lycn6C1ZDxMaTJJmqY8Pn5MuVqSr6e0DvqJQklDFHmUb8mUJBIOFacslWNdHDOwjtRJknSLMi+p3BpXLnGFQY/62KYNhVDrumKaroHQhRKIAEAVTn5qguMP9fKOSLb0km5wpBUbmwNOF6ecz6boyKCTmKKsWeQFZ/Mpy7wKyVGRRhoTfg7nKJsaXENRVAgBcRyjTIQyl8gJSVkUHWvwk8+nT1MfaR2Go0op8tWa5XzBfDoj1oYsjq5qnzSNsTYUz9pIkiKoHaIoYp2vuHv3brBg3bqJ0Tak6xAm/U64K75KUEEFZp0WMhyttAmDJiGJB0NaUyLaBt+0IBQxgYUntSLNMtxlU0oKnLV4EXExm3J8PqNsPUiNFSGFSmiJ9QJrXYhq/u5b8ynPfUEA6Vrrmc+XV8NH7wXeBSXPJdPz0kqlO7vtpU0vfLwL0dD+UrnyXMPm6jX50WMt/mXX81aq5/mfn8bQueLZAojOvqUlEYb5csYH9z7mwb27/Iff+FXOz0/Z6g3xNqzr1WKGVIpVvkANzCe+ppHPrNCy49pBaDAK6bHegRedGs2jEGxubiI2YHd3l1WehybOfMHT05OugbmgsW0IPKpymiYoXFwT9owsiehnCSoGL9oQCNQNbowxSK2Yr5bM1guGgwFREjOaDFkXBZW3DIzh1isvI6XmhRdfZnt7O4QdwBUgO47j7/te/UHuz6ddf9Pqwx94rB0h8SrQz6XzpEnCztYWzjY0tSXqmjZ1XbNeLljmS7xw9LMBcRZT2xJbWvKyZbleXaVP9Xo9lFLBEoKkKNas8vzKl39JzgY4PnmKPtAMe2MG/WF4gM1zFus8gFHjHsWy5Hx5gW8sg17GYDJmUxuW6xXz9RoTJXhvqdqKcObrEhSEo7UeoXv0JgZvFqwWc2rXcjGb0k8yXC/Fuhprg1rFdRPCcrUG0aJSha09o/6A5WJNW2piMu4en3F69g7gqJoVxu9SrWZk6QCHRCiBI8BfUxWx0gIVGUQLQ9XDtiWrPEcWkBGRYnCto6lXVM6R9gZIkyGERGlJU03J51PiOMXbboHWNf2tLUDSVi3WK+p1RTFfEscxToZOvVKKXq9H27Ss2zVCdYA3obHWkud5l2ASDtm9pEccRX8zq/KvdIm/VJXxyY0hpGkhLv9eAR4jW7xtSHo9tg9vUgtPVeeIyFF4C2isiPGyxpcFuADhMjcOKZYLartmPp1SNRUrG5ROTW1574Nvh+mAaxB6zWQ4Ikk158dB7Ll/uE82HGKlwyR9tBiiyhFZss3GsuS3/ugB7z96wtbYgp0zm06Jechbn/88OzsCJQZhqiI16wK+8fZHFNREJiHOUr70xZ/kp37273C4v8fB9c+BinHCorSjbltmFxdIKSmKkjt33qff75PFCSfnBdeubdKUHtwapTSvvfwlbh2tWaznnJ+f0BulxLGhFZ68rdEmJZZxeIj7hNZamiY0KIQMxZ1wBcpW+MsHs04waYZDoExEf5AQR4G75JwD26lXuiItFGs/mh0cgWSQjCntGudtyK3o5OOCAJEDg5I6qAJssOy5BrArtG24/50PGA9usFoDdt6t0W5TvmrqhK/rvMV2PQbfPgc2lj3aylIXNYIGT816vSRKYrI0Js0EuJTWe0ajEZgBXkU0XpLoHl5pRJcyIKVmvLdNNT/jzetHPHjoaD6uSA5HnL5/n+0vvE4xGbCYrXh87z1+6Y2/x34VsT3c5fd+518hxQXv371HIy1bGyPAIuae5nxNlsmg/nMObTQoGCrHMBb0VM3uOGYQW7RrGfUnTJIhrl6yOR6yfLCmT03hIDOeLBHsjiesVwvyk5LtcQ+3tsyXBZtDjbUlaQQbGRwMNLHSfPHWC+zt7fD09IxpXkGc8uTOh7x4eEgUrVlb+PjsHIvHWYl1IeHDe4umC3fyoUGTWsve5gZlVTBbrumbmNOzY8apIdIwGQ8/YSP4bNYjWBGsG4nWbA5GZEmMk4LcN9Sy4cLnVFGDEzHSxXhVUbqGVVWwLApaBDbuoRAkvT6NaxFSECVRAP7hWVZLVtWSuVZXku7aWlwkcJFAFyO09OAkbVOiZErUS6jaAa+++AaPzu8yybawqxX9fv/q0HQ5HAjA+qDKC++DYL1EKqROOn5efZX6d3VoumzsiFBcXIYiCGWCWseLK6vWJYMPJOcXZ5R1zUuvXuPrv/8n3H/wEfvXb/NTOuHu/fe49coLJJOaB+/PePfeksOXB7z3rSnSxyTJiH/xv77Hr/yPY26/+iL/y//0AQcvwOxkipeCyeYmJ09OMFkEuWP7Wh+V5SA8o3SCXRQcPz2jHq7pbcaUbcWH736T/Zf3+f0/+22+8vovMZ+es3swofrWEkGKbx6y2fsZkkGF9gecP5nz6OyCL7z1Y3zw8R0Oj/b45p/e5cmdU268eItXX5/w4KHnYvb2Z7sehcALh1Qq2GiE74p7TXebg/XjqnujQ6EvbTcECDwHIQRlLNhUQ54MEjbcmiaveWnzFj+x/fMI39A0orNJaUTaJz28TewcngQpIe1tQjvBS0stYqK+ZozCNi1lXZCpBa9+4S0wAyoBZriPHuzh4iHV7EEHvM0xyQBnPUmSkOclQgnS3oDY9UiyIVW9QkpJU1VIYTBxStNWWBviqZWrg8pIK3CSSGiiaM3FR28jlhe0iwu2h32cy7EohtmE6f07RFmf8SDh+OFjNkdDsq0Bd9/+Olub+0y2xkT2GDUY0cteRZZPEMkQISbYqqSs1rR1hUSQpjKkr7WbVEWJ79JX69ZRLqcIGlqdQpZiVJ+j/gEozYNyytb6nJP8CcOkj+v1KPorCl9gNzfx5zPUMKEucnQS4qytbRC2S3kRDu3VFdPj+7hGfiiXkpCIGpEIiqJiOptR1jUmM1B7juenfPjNu5yvIoqioKgsSDBG45Wk8Q7rHdaGRpx0Pqiwu+CT5xsxRVHQNPZ7WKXfr4DT2oTzNY6Tk2Ocsti27shyIWUq1Qn9JDC5YiWp6oKmyNGMaZqGxWLGh+++y/LigqHRbO/ukW0opK2R2mC9oO3SCEFAN0xxSiO8wxuQSqMQiLSHLwuk95jnlL9SBHu7cOEc4qXoBu8Vp+fwjffuUdQNBy+8Qvqt92ilpLQWbWSIXq+qT73n3xtCAs55hJYIrVhXJVbAfD6ncRZpJEmScHJygtYaJ1WAVHuPWa5obXfuFOBdi2t1sPh1MeOtcGFQeJU46f7aYNm/reuK+QkBxYG4GuJ/2vU9TgkRkgEr57h+eI3FYsbnv/AlcJ7VxTlHN6/TNo5s2KN90iCFoKlqlJSgJa61BHGrZb5ckmjFcJCS9lOkCtZoKXyw0KHwvqWtW6pqhlYKrSLG4zG7u7uUZcnRjWuUdc1itaZcLZkuF0ynU9ZFTp6vwUkSqdmaTOgPeiT9iIYtFnnBfLEAKTBZwvniggePHlJVNV/+yptsbm+xvbfL+cUMpTSm1+O//e/+e7J+j7zKqRuH820IZBHPNcP+M5stP5JNHO9BakVjQzPl8HCfmzdvdjDfAF8SQlwlUVV1UJ34jgEhpSSKghyrbVvm83kn34fT4xMWqyXCw3g4RHV8nMuDFnD1AiulKNYL4l5KHMcYYzg8us52XbNarVhOL0I0eNNydnqCt5vUdR0aPJGBpedPvv5HrPOSF195mZ2dLTyONOmjI9VRziFfLVACpufnHB8fk69WpEdHKEDJiLqoqesWKTVRnFKuS9q6wJgYISQ2CbL2Iq+oq5bVesHTp4/p94fcfvUWWdYL6gLnSVNFa8NEv64tQvouZrBiOp2GP6cJWmuWHYV9MBgQx3HoQndR6Plqzfn5NGwoaRQ4AISFo7XGWs96sca2NUVZB1BzWdK6JkTIGh1URV3M+OWh2lpLZBK0EldezLqsWCwWKKMZj8co/Vf3AH4W1/fKMz0Ih6BTfgmHbcNmlMYZB4dHOOf46OP3mc8KmvaC69eGCFpcWyMV1J7AOkKgk5TBYMB8PqNtLE29xllom4ayDNLp/b1dkiSjcZ4n9x6gI8PBwQE3b7yMlJLlKqcs62BFzLbpDzc5PBjwK//4v0Z6zdnJU95/5zu0bcvT6ZLJzhOy/pDGWWaLOUmSkY4HjCZjRLHkp3/q73D04gu8+vrnOLx+k2FviI4SoiRCWQtK0q7WnJxeMBr2iZRmYzRha2sLLwXHZ6fM5wsya9nf36c/HKNFxGIRNkBjZACOAlVTo1WMUWmwjonAnoKKGk9TVkjVWRUwzBdnlFWDl4oosgy0Ie31Ozi2QeoOVipAXMGqQwzqj/TlIY0z6rykbqvgHPAuFBIIBAopFK479Hkn0CpBxIrbWz/OT27u8d63/hXnzOhvH+JncBnJK4TA4rjSIIsOZuwv7T7PIsaLosDaCNuxypxvaYVjXa/Q3qNTTZWDcEFVmCaKuJfRnwxIhxukWUbTVDhrkVZQrWaYnuJMl9z60pc4PXnCf/XTf5f98W0e3F+wms3oMeAn//4vcPbwY0bDI+6dfEjTOJIk5XO3bzNbz1k8esxHx6chdU+ExoIwgrbIiYVAxZqBtEzSiBtHG2SZQjjHxmQHW0q0Aq89yld84Y2Xee9hwWndkMSavd1xaIjZASZZcbYoebpcsr0zYXoxZyeFgYp4c7jBF18YMMgUe9t9vnH3Cbo3QicBpr/QjrWwPJqfo7IesrVd4LUgUgkCSetrjNAIr2gqx3C0ycTOGfVSFss1Vscc55b+oEc6SPB10UH/P/v1GyDjilgoUh3RtC2Fq8ipWbmKx25OLSRaKaSMaNo1ebmiqi2tFbQEpWsURUgjcaUnigwmMbBqEMKxrgvKukQnGmRQn0o0RvTCM1a3WH+BNB4RJbQYhBsw3p6wrB7zzre/xqu3fprr41euzgyX+41WoV12GSfrvcfLLibYBzuVVoq6bQK8WYdGTbDl+Cvehr/q60iU1sH2Vbchya0oSbq4cGstsYnI8xWz1Rm/8A9/huXFim++/we89OZXeGv6Fr/+H7/Gg0eP2Hz9FDl9iYMX1rzzp6DEBWVtiaIev/ovK/7P1WP+539a8Me/cYN3v3HCe9+84ODgZU6enjDeGnD3/Y/Ia0WmE6TSrOqKjWxMOu6zFMesqwXHs1PGUnDv4V1yA1K29FLF77/z5zx9+pSvvvZlVheP2RzWPP54wrWXe5ycPuT1F29QtwVvvnWbXjamF99mOf0Iqz7m935nxa/8k3/Ab/8ff/LZLcTLCbCCT/ooLtVRgSoqnAiZH9IixSXbiG56rPE+7JHSOBpaHrRPuT18GbExIG9rHuVPaFxNj5iBTNDGIHp9DFXYu50FkWD9HNHx1pSsSfo7nF7M2X7954jaKhRDolvPQlJ3zDfV3yJOJxjnaPHUle2sd00AhSIQIhQuUZSQpgPatqaSgWp+ef6KdYwXIjSBhQclkW1gJiESfvbn/0tW8zNWT9/HLs6ITI8yn3J2WjLeuEZvNCEebLPxyi6WlK29Qw6VQcUxF+cr0r4ibix2dQb1G9jynKZYIjLHhtJUyyltvaRcLMLebGLWRcn0/AznWoY7I0Y3rpGlKW0/I3ctGYq6VTQq5ax5wq2tN7ju3+A8f8TX7/0WjWlYLi5gkDJxjkZZVAeGU50SpcEjnQj7muoatJ9xzeysY3p6xmiyjcwSMD2enE05n53zwb1jzmYz7j6pqWwA7ZpYgVToKKJqatq2oWmfxVjXbSfS9KHhEBqBDZe18uUc5vmiMFiEvre4a5tn0GPbVkRasM5LskixM9lkazQhkorYhOHqoBcSfrGW84vA8twY9zk7O+Pxw/u89+6Iuq65ZiJMmqGERylPKwwgcEoR+Sj0xC+VjlJhXYuXEucUrbxUzxmk8BghELambaoAsW0a8IJ1XnB8OuPBmSJvIoZbuxzcfIlvfuc7LPICFWvaqvlLVdTPN3IgOCLqJjTNTqehpinLkqJtQh+oKMH06Pf7RP0BD4/PsISgnKquKavAa/UuQHRkd0+8CMaioMzsFqHnM20o/k1eQgiEFIEdJgTW2e/5GOm/Cy8B2NbT1jnDXsYrt1/lS1/+IhcXF3z44R0e3PmIhw/vcePgCFdWRHEMjSMvS3q9Psll3WktkVFYV1HWLaYK/4/zNdPpOWnaC/wwIbCtJ1+tWMxX+C78KI5CaIf17srhMjs742I2p6gLyrLE2RatFP0kYdBLGfZTktTghKeuagySyjmiJCPtZSyLHCcFL732Ov3xiMPrN/j2O29jheRg/5AkS3lyfk6BRWqB1BrjdTcsfdawfF6J89dV5Xza5333Ov/PvX5AJY7HeY9QitgYNje3GY1GIdrbJyQmCk0XIdE6op9m1P0a59pOZgW2tkRRhFCCfr9PkiRXDZ3YREglKNsG4z1VVQXbUhyT9jOAK5ZL2QS5VdPWQdYVJ0QEWaI2MWkchamDD6BELRX379+nti3b29tYazk+eUJ/NGQ4HqAvJ3mXID0Bla1pG8vJ8RmnxyfEccQlU8a2gQ+jZfiZahsWXL5aIUROHKUMB2NQz5Qrq9UKhyeODZPJhCiKSJKEKDZYGywSbRsaNEmSdBNET5kXqE4C/jzhfrkMyV9lWZIkGYFxU5B3/CDrL6Xi6go8LLp4RHSE9xVFXYXIYhui7ry1tK29apaVZRneqEmKdy3WKfD2yjdYFQXSGry9zKL50bq+lwwunwGqvMS5trMZCbJkQJaOiKMe1juaLqGgtYqqba6gx9ZahJfoKGYwGDAcj3DWM5utWC1zGu+Y9DLSOGGysYk2nUJJGHq9AcP+CKPTsH4bx6osSeM+UsmrNIdrN27x0z/7czx8eB/nYDlf0FQfYqIUYQxVE9aUQzAYTbh2dMjJ+QV7B/sc3bjB9vYOaTogSpMu3QhQQT5rTMzB3n54r2gdFFxSBs3Iq4bWWdbrNWcXM6rSopShaSqqtqKsPa5cg3N4pRkNIrSOQjpd67EyBPB519I2Db4Nqi7fFOR5SVW3qCgOUksbLGu6KxYDlyLIrIV8NoEPl+RHdZfVHuIipypWOAmKGGFbrKADPWqUjahdg0YhfI1ULbWr2H1xD5fPEApG6Sb5fIl0QLchC2lRTVCC1JcNrVphfIsX0FpHXYffGzdgOi8pygusP0WQYXyM9i2q1STCUNUF9WoNKGptkaalLmoGWyZMrDxEJqG0jsHuHtV6xUa6QVt7etkWPk7obcBoegBiTlMlVMWa5ari/PwD1hdzbt18k73dEZGWqEoE0Vvb0PgaoaC24blkjML5ionpk9oM6T37xZL9l1/EujpInXWImlY6RauIOr/gZ16P+dMnhlWRIKICISriakTfzFkIi3cNdb5kb9xnbEv2e4KjHcfe3i6JjBFDT2+4QlBzsigY7e/BaYRoDXu729w5W1J5TRIpqCucXeMwWAnS58RYBn24feuc6AwuGs29ucA4wThtaFTCYrYikgJnHI39bMNLvehA/U2L1zCzOYNa0aQrVqJh1dZohrS+DQkoqmVde+rWMCsbqqpk2B/ghUaZjMj0WKsZ1jjqJkLLmKap8SYl0hG0FZGOqH1L2dRIVRNpg2sqImkQjUNGhqr1pMkrPDp7n9Yt6WebOD+k1CWrYkUv6WGdDcWTdc+xbHyXuCEQMqRXOecQSnwiYvzyMh50FOwHeemJ+kPKskQQlDnhzBCsWEpGBPaOZ2MwYrk6Z7U+p6iOQCd88fZXebo4Jl+fs/fSDud/mtOeHLB59DG9/i6tW4ZEpbhmlOwxX37Ai7eO+O1/fkh08zeJ+gqilOG1I/jj9xiMeuALNjZKZPXzxP0nFPIBjwuLdA2JXrEhv0CxecbDecmm2KfptXzz/d/lpZc+h5d3WJUnfOvxbxDtJvioz62jbX7nj1eY3h4Xx/fZ3N/lYjlHxYK8WDDcOuDx04Le2PKtb94lMuYvWD0/jMsFS83Vwbhbpz7YJbzsGjlowF8lVAkfuBVOyMD90wpnHYVt2EvHnDfnSFtwOLjG/Yv36CUpwmyQmgGJlVifQSyQraWpclxzjBeGZLiLkzHRYJuTi4I228apOFjZUbSILt3FBbu1UqH95By2acJzyULlG6Qw1LZECY3REd62eKMxzoOOrmwG1lpoAiJA6MvK30IT0oLCl/f0Dl5ADbcYToa4fIlqSoa2AlfT37lF3N8iGu7Tm+yHRBZrGQ6HtM7iZI1MU2wEWsVgWxT7iLNHtOU5vosFlsoEXtnc49oSU69IIkndcVayyTY5NdNoSYHlc4OUKgmpkYd7b/AnH/0eO70R1kx464Wf5n79h3zknjI7nzM82g6gf+/DcFd4xOX7VwiE0zghwHVsjs9wT7etpSxaqnZK2hvidUKcDGic4qN7j5mv20uBNtYHlauOJFVTU1Y1QnwycEFrrpoyIejFoxRhX3MOa32n0n9WzH1aAwfCe2G9XtP2h+zu7pImEfN5WHtxFJFGMZPRmCRuEXgiY4iMYTwaYNuSoijCQFVC29bMzi94aAxbO7tkw2FQNZswHJHGILVGo7tnq+3OyTKAZwnfs/r/mXvzGMuy+77vc7a7vb22rq5eZumehTPDRaRISzJFS5bk2IaNSHAQIwqQ/J9/AwQJghj5I0CQADEQCIL/sAIkRhDEESLHoSWZiBYkgkhKJilxyOHMcPbu6a32t97tLPnj3Pe6pjmUOBLV5mkUqrrq1Xuv7j333N/5/r5L2jWS0ITgaJ1FdnKqIulxcnKCUpogFHceHHP/NKU/3ubZF17k22++ymK1wvrI20Q9BEyEv+Dpx0Opz1oKu/5ZEJHl5APRvNZZ0JJgYxioIlAUBdvb2zj10Nx+fb8QApomJprqEFUWAbAxNhClNa6JoREmEbTtj2Z9CRdYHRclwmLzwz937+XFB+chdAqkrsmaqAzrBUIZ9vf3WRyfMTt8QL8/ZNVFtucmoani162zJFIRTAT4mlW1qem11Li1xAqFsB6h1WYvbtvotbSaL6jtYXx/3hO8oMXSlBW1bePeq2lACooio0gypJEE21LX0b+2tQ4bPE1TsbUzodfLOTs/ppfl9NKM8XiLPE95/bU3eOqZZ2hdrFFi8IpCaQ1tZ4EQHsq3L7LCLs7VjzIugkHr5/p+oM5fZnw0T5xuY6GlJDEZk8mErcnOxiCwqioUgX6vh1ICk+cMrWVRrlitZkipkUbiqhatDf1+7AiLAL286DrJjsViEXVxbUuvX1C1NdP7M/I8ZTTcIhvnCCyrxYIH9w8ZjUZsbSvqVcVyuWSxnGHShAEDvPcxom88JjtPOTw8pCgKPvWpT/H0zRtUZcP87DyiuWPJ2dlZRBvznCxJMYVm/9IO5XLWAVSC+/fv80df/WN6vQG7e/tcvnwF5wNHR0fM5lOMMYyHY+7eOYo+BFrRto7Z+RyPYzCIBk9V2cTiUgiyLMfZFqWi70zTVl2ct9wAR6VdxsV6saKta4QIm/jUk5MTqqYhyzJGoxFt2zI9m0XNqFvSNBGsMEawWCywbcvZ+TmuM4Q2ayO2Kvr1CBEYjgZd3GKLc4HZxgMppVxGUGpZVmQh4Hz7iHv3j9642BFZX0xZlhG8p25hMrmMFClNY1msziLAYBRCeERoqaoW2zqa1hHaBiE93qTkgwFtbem1kTV2Np3T2Jb+YMT1J55iZ2cP27QsliV7e9cxacGXv/I1nHPsXrrKctHihp6tyQ7SpMwqi876/PUv/AKL6Yyd7Ssc3nnAv+l9mesHB/T7Y+6fnHB4eEiSJOwHwd7lfUKSceX6Na5evc727hW0LqJxpg9IB15FTbINgvHePraJ7K08lRs98RPb25ycnxGC4Pbd2xyGUybDLXrDHmnSJ1UF89WSlhYRNMtFw2zW4NwpUmryPKXXy5FKkOcpVb2kriraqsQJjdSaosjRJiXIeDOQrooSo9C1Q0SM72PtI/MjPpx1vP/e+4jdBCECdbMgJY3eJPJhcRL/BZQ0HM9PKOsVIlS8+to30UlC1UikM53HmCIEh7UVWrSbFpIPFgksVtHgXZkUk8ZNbTkrsTZ0AImhbSJ7alkuMVl0JZBSkO5tkZiCwd41+ju7XHrqJsIHympOmmVILZjkQ85O71Et5vT7BWVlyXsZWVZw8+ZNjm89oNfrkU220OqUm4Mn6Rd7lGcLtiYDoKReHbFanPDg7j1iaHBAhUCWGhJtuNzrYUNFlkr28gFF0rJ7uU+SJwxHExaLc0ya0u8NKJdLymZBf3tC0iQ8P8p4781z1GiESFP2sie4feLQ6ZRxWlOtSjK94G+8dJmn9ob0RhJLyaIt2dI7XL3+JKt5zRvH7zItl+zZmzZQAAAgAElEQVQ/9SS/+/Wvko7GtLMlFgFGMyhSqiqwrB1KSZTQ5Cbj8M6UUZKjtvq8/OYtZgvLdn9EkWTcPTtnVPRJEkOep5jlYzbjDoG6nYGSCC+5c3LGsMhpFw2lrWgDBHQHCoJtS7wSqCwhaSIzRUpJkWX00oyqWqAJKOdItKdsltRtQ1ABnRhkmqO8JJE6dm8VmESRmpzhQKNVwPoBs7nm7uIPCMKhTWBaD5mMnkYePcf2E9tALCjruiTVHwQa1gXQwz/xgwXSxe9Z71jNVngB/UE0sF1LqtI05ejoiKtXRpyczMmyBOfiOtjvD7hy5RorP+Py/hVcowhVn8mlGdefPEOp/4/l8ogv/f7v87G0xxu3vsE/+pVf4r/8j+b0woBUT9kuXmRZv8tX/9jwt55/iR/76WOOZoGbLxzzh/9nyyy8zOSax65u8tKnx7TuPvffusqrb77BziVN1R5xdG/Gpes/RbtXMc9LpsfnrNqSw/ltTlYzPvbCTd6/f5/x/pMcLm8TgsDZc5pZw8gkzO6foQvJbDbjuRc+xXffepMvfOGn+PrXv877d+5vWH6PY4hOmrk+Pw9HwIdm86g1SK+kiZs3sWZixi5ykDl4T1IULA/vcTIwTEb7IFPO7ZyD4WV2GJBle2inkN6CXRBcwEtJ2H6KQgS88vjW4azHu5RLzz6JFDmtMRiZ4pGkKjbrkIIUR/AxAcZlBcZ6rG+pVkuM89R1SV0tca5GBInzLWnWp+hNEFKQ6uhHGHBoqWidQyLxdR0T1LRkUTYbWURwkBVDvClQ2/GYpQqESch6W+hsEH0JjUF40NIzLyNLaXj5gNPjU0aDPiJVmyRZs69J3AH1akl/Aq6uoJ4id1tCWxPsisHilKZeoYVCSAta8US2xSJMeWP+Brdu/x4OzdXJ0+z2nmXh5syWb/DGvW8iq5IXn/4sZ6KEYUJ+8oBVW5IqTfAe4QNJENF3RVokCV6Ct/axVoytdZzPK3RmECbghUboHvdPlggzohhLauexq0VXc8eocYD1JRPCwyo3EBuC689pFtcY5xwueuGTJGbD9PuzhrUWJSST0RhRr7h3532st/RSwzDvUaQZvrSYHMaDtfTUoXd3MMKzWCzANRgZ9wrHx8csl0t6vR7PJ4rBcIzxHp8WBOtIpAHvuysv7vA3Ejc8CI3s7g9tCEgkqvPFlEFxdnZG0R8znS547/0TTmeWYIb8xBe+gA2WX/8Xv8GyXMRn6NRbsAZp/AfWgosb5bVFA4AIghCdcQg+YGUA6zqmkMe20bczSRJ6ky2+/vIr1NaSFD0QghRDVZU46yIJsANy4kfANS1JIvGtJ7EBnT5ucPsHH49u/tcgzvp73+P/9n3Go88RmfPEkJ7Gk+cFaZrSPlUyyjNUomitpejltHWFUJLpNCoA+oMBzgXqahFtT1QM0qnbBucbEpOjE0NQ4J1DqYQ0NVSdKfiijPv8YB1ns4eqHJ0olDKRLJLmGK1I8wQlwYeGRdXim85nLsCqrXGtp9cfExycHJ6wPdliMtri+WdvcnjvhNdffZ29/SuxriZK8ax3BOeRUm0IWY/WGT/MsZG/ie+Nhv9QoOgHXBw/Iogjohs4Ebk9PLzLZDLg6pVddBf5tVpVnJ2dRfZGxzRJvWM6dTRNBGkCEu8j/b/f79Pr9xAhLmQBj1SCopcTQhK7zCH6y6SpQUnL2VEEYqRKWSzn4B1JZ4yY6Bh7rYWivzXm+WefJQRL2ZSYzLC7u4sNniTN6PUzgo8gSZaktE1LlmXs7OwgpaRpaoLz7O1ug/Dcevc9vvqHX+b4+Jj5fIFWkS1TdIwHbXK81hSJ4Sg94ezsLCY8FRlFvyBJEoKQLJdL7t69y/b2hDw1eOdZrBaAQEnTJXXVG/BGaUFwHp0axmnKfD6nLEvqbgEbDiOLIc9z6rrtLrII5kBEFqfTOavViuVySaZNjM/WmuViwXKxQGvNYDDojE1jtyx4GAwGuNzjPZuN/to8zBgTwbEsoprhR0xT+j2JJ3yv9tZ3N7JE5YTUwVCyNbmE1hKTRhDHGMjrlKqxyM48urQOa1u0yZlsXwLAbjfUdUt2/5jgYHv3EjopOJ8tKMuKIBJ0kqNMwaWDJzAm5dLeAasq0vttC/NqQZCCBEmW9iiGW3ziE5/j7Mop7966yx9+9Y8oeglZlnB+corWmlfffJuqDgwmYwajPQbDyP5RJkUYjfOS2reUi5qqqlisKmanR/T6OSJI2qomLfIYLZ1l7B9cZvzMDpevPUHbRmAxMQVSxw5aVVlCcDilcFWDczHNqG1r6rpkNj+D4IgEJBs/hKfoxajVfj9H6xRkNDiOyHf0wPm+0QU/wqO2LT6NKTqtbUjznGA7rwfBugWCBKqqYnd/m6PlDFSOa1c07RKdSpTo0e+NcQhUt/GxrmS5vEMgkClJa2FWRp8rrTVCaWQisNax8q7rDEr6/T7Lhady0878TaC0iMaStSUhkPmALmuWh8eITNG0K3q9FB88tq0osoxCC85PTxlNtpAmYbmomExGjLdmjLmCtwV5HiMnE5GROg/VGfPFHbQ7h7ZEugrpPUZJekXGIE9j4VXXjHaHPPX8k/RUQkrFdrZg1Va4ecvu3jbzWcm779/h+tXLeCM5L2vOSssbd5f0y5ppM2fJjCIv2ROnKDHnhY/tYZQkSxxjlZE2jtFwh0oNKRcVVhistyzLGlJJJSx/8s1vsbd7mWlZMsgLquWKtq5wQdA2kQYfPQQqskHB9Ljk2WdH3J1rGhvYHY3opzmjXHL56lPcvXs3zvncwGP2xJFC4pualXA0UlG6hhNbgnQ43+KRKBnPgZAOH1oCBiEliemjpSHTAhpLGyraek6mNNIKciNpXEBaS9N5NhS9PfKkM9cuBUpoekkvauQLh9Lg3ADlwNuGFkveN5Te8913X2P85BNIKamqijRNN42L71dQfYCdc7Fzu+ngCqaLOUJJ+uNtcJGhu/bfiwkYc6SKxsZ5nseNVtnS7w3JkhFp0o/G60mL8QmTrSeY3fxJytWQQXGTb7z7L3H2Kv/bP7vNf/rf/zV+7X/8PVaHI4Q6YnEsuPK85xtfewPVXucf/iclf/AbczSWGy9eJqFiedzj7vEbBPk200Sx9Zyn6Eua9lmyQcJXv/JtPva5CcyP0EvLwfAZVCH5zp+8zs0XP8bb75zx9T/9ClcPbrBcnWEGFcM0Q9eKduF5cO+QX/zpn+dL//p3+MLP/m2+9rVv8OzzH+OdN9+hqo4f42wM4EXsqBNN64XoEt+kwNs2btaUQglBEApFLD5CENH8ViikDCihsQFObcNEDzlqThjIlJEa8+7Z28zSEQdSQXuOkgVb46fRMsUJQ1/3uqTEzvhaSiwKlU+Qymwk4krGe1xk3YH3IkbUe49wNm5smyYmqKKoNs226DsYo3xV58cR6yiTpUwPb+HnNf0rl0hkQaMCDo+tKoo0stHXdgECSZJIEp2g+2OSLogAqUiTGASC8HgXfYWSNLLcvfdMtidUq4o06yHahuA9qCH4gMm3u5onmgzn3uJtwFcrkuUhlOfUi3PAo3SGmVwiV3Bdpry3e87h6j3ePnmF+4e/zbmvyPtDBmY7esq8/T5qWLGYanZRzEPscjd1Da5jCZFFtrevQUa2y+Nk4iijSCcFo9EW+WDMt9+4zcuvvMq3Xn2DfGefTGlu37uPc+sNseo2VhGREQK0lJvUmlXZfOD510DNenO2Nkdd/+yiCfujwzauayqmrFZTjh7cBRXZYEVi6KUZwXpSkzAa9LHW4nyLFDnOjsizFCk8R0dH0Q7CaMDznW+9jPeWy1cOeOLmcyQywbuAD5pg9IZxv2mWdQwYFWIoAHSJxMI/NCAPjjzvcXh4zqJqefWN26TZkE998pM8OH7Al/6fL/Gn3/xaZPVm8VpLVFzXldA0TQxFWSdLrdfxeMwfYSp0/7chgoEhhFgzqmiWO51OOT4+phhPSNOU+WqFlwofAkUvQwhom4p27SHYpXMRIMs0rrQYD09f3eXOtPwLz62/6vHovLnIxAmPPu77gRCPfN8TUF1imZEKLwUSjxQJN28+S7m7y/wo3iv6/T7OaIINzGf3uXt6zv7lywyHY7Iih2BRSqCIAHldNqyWFavVkjRJSJMMn1q00hHYCeFCiInHSEWFJ4ioCJBKkaYp/VERbTy0pnUrrLfUZUXTum5noUAGNCmL8zOqsmHS6/G5z36GZ56+wSvffJX/49d/nUVnHVItVoggMSr6+K3XW38BZP3LGhp/4Fx84PB/0Ij64lgrlf6s8/X9xkf0xAm0PsQ0Bgen03OOjo5YLBZMdkZx0Qowd1EGZa2laSrqJnrHWBt1+nVjqeuGtqlJkwlGK1arFa1tOhfrKOdJ0wJFBH6UEmgj8Q6WyyVeeLL+AC2iMWFdtaRZBCeG/QG2aUlMitaC09M5i8UCmQp6eZ+qtThrqSvLaDyJx6t7XSUNAoUQgUQbTKFwtmE3OJbzGW++9jrL+YJyuSLNJM4FWrfE1A1SlZikoNFw1LacnZ1QNTX9PKMociZbA/Jehrduk8jlfYz3Do1DKoN1DVXZ4EO7ccpOjKBxFtlKVCLjBqjXQ3emjNOzc4KArOhjDFRVlGQZY7rYw8D5+YymaSjLipqSxBhG4zFJkjCfz7HWRmM1aTYFb13XFEXReRM1m1SctZQqLr5xorUd5e6xjQ+Z3xcvvkc3AR8G6GweJyVKi45imZAmOUmSkBhN69cRwZFtZFuoypbz6QznLalWGA3ON4igkDql6A9JVMKl/QP2Dw6ivM1UKJkynmyTD/soErSU5L0RSlU0FlZVifXRbT0oHVkqKLKizwjJ3qUDZquSs+kpRZFRlQuEUngn6Y3GDAcT+r0hUmd4JxFd2phUhra1VI3F2Rh13TpP28Qo+fl0xqUsMpCaZsH2botW8TgoaWh9ifUtyikSbUgz3enAPaXxOGlRQSBEQqCJSTY+SsUCLb5t4rVvUpSUBKlAyo5JoqN3x1pOJQM/hPXzsQ4hBWqQUIcGgsQ1AaFEDKeSscPlxcO4TCkUOs0Y5AWnR6ex4O/lJDLq3IVK0MogREAGjcl6tFUJzm8SPbSOJtBSR7PWaPoe18r1BkKIaGQougSGGMnpKBKDdzUqWBKjGfRSlm2NtS11U5EmBdooXB2v86IoUIliWS6pa89wOERrSVEMUWJC00zp9/r4StIGSzk9w9VLev2GXgG9nqJtJKkx9LKUXmrIEsPWIGW8O6Q/GWC8IZMJ0i8Zjcd439C0LUmWsqW3KJua1lksOSdnLaUt2NGG96ZzVj3oXxtx41KPwjekWYLJDD5UFOkY4Vf0xmNsm9DOGyrrGG/tRAacAC8CIkiapuH8/BylUgotSYwgSxUhtIhacGk0xDkoyzlV5Rlvj7h9r2FdADvbkiU9zo4OsW2NUGDr5rGrAEUQyFbTyoagwAVFWzuUib4ycUMo40bUO4T0uNbiXWTqSaGI3oUKKQJVWTLM+wgvMGhymSJVwDqLrwNOgWs1QUBKTj8fMcwGDLOCXioREsrgcLLGZjVnyxPK6ZTgIDGRueq9J4iOBi8/qEV/dDxKxb9YEMUiNnoCyU5+rKXe0OybpiFJkk1DYi1PllIilEImmizrEY3wA0KXtKWjrRV5Oubatee4+5bm4Pou02rJn3z5iFe+k/KL//FP8+v/5BXasmJn+0kOrjneu3PEO2/OSAfw1ju3SRIQPqVZZPRGntKprnnyPHbxLaarQxSWywdbtGqAJuN0dsxTVy7z5ntvcblZcOvObZ56/hM8+dRNyrePmZ7NSPdT6rBkkMc41wfv3ccUGa9+91XOpjPeffc9VmXL8fEpW1tj0vRxBhBEr0T3YeC8v9hFjsmPUXoFdAlGdH5gIYDSijrUBGlpqwVeaYb5JU5qx35vSF+nlPUxlwfPkZsD0CO8XL/6Q/aR9XGeZf0RLtAZYyukEPgQo5dlCF1TIXqYQeel4WPiSpIkcW1Mi24jCiIRiNBHmC4yPQh8u6SuFpSnD3BeU333lL2nPo7zS5wQZElOsA6je/jQIHWCMSngY/faaBQKlWqUzIDOYBaJ1jJK2/3DzcE6WaWtKoIMJASsddEGwQuC0jFSGkmQMdLd6RxT9JG2QS5Occ0SJQ1KGVSvj0s0l+U2RbnP+9N3mCYzZGWZL+8jWsv5YsEN3yNdCN6vTyh2+pjGUzYRwJEhxASkEJlBDhHJHo/ZK0wqSTZI0blBJ4qqXXI6P2O2WOD7S7yQlNUS6UO3Jqju3XZrTFQcbdaLR8d633JxPVrX+GtP0PU69OjY2E04R1WWMY0qRPaW1prMJGilUHF30gUQKByQJykKQdPrsVos0FKyKGPzu65KTg8P0VKwu3dA1h/HZ3ABLx1CyvghuvcVHtribmpp4jmM5r82psQ1ASk1tm2wTvDi8y+xv7/PF//1F/nmt/4UpQRJ0jVJQ8CGWMv6D5GTrWvJR4/Rpn6XgotFoSds3mPbusgKOj7evN+6rglAmpkP7AH8Iy/tvUd4SA1c2bnE/fmtP28K/Vsb3w9UiBYgH9zzfJQhOgaW69aV2Ef1oGPqtOvA7eBsZ30QA25OzqesVquOTNEBl87TCodQiiLNOJvP8b6C4EizPP6+imx9iYh7rCTBS0mWW4Ts42ETW6+NItWGJIvsTBx4b6lDi3WWxgbQBu8EaSJwVUNwnvF4zLX9A0aDMV/6rd/mzTfeoDccUq1WD5kwSETwWB9i+MIPcKz/suOH7YWzHh8JxPHBUxgdAx68oF6VnM2mTM9OGfQKEqnIixQfevFitG7D+CgHJW0Zte/eeRKjGQ13ESLKe5bLZXxDWnNpZzfKXEK8QK1rSJOcXq+H9z5KO4wh6SJGE20QeHyX1JJIhUwjo+f27du8/ubrzM8fAhWTnQlFEVkB9x884NKlS2RZTgieIktAOE5OzvCu3bBZjDFcuXTA888/Ty/r8Sff/DarZc1gaMiUoWpb2mWDMjV4QWNr6npFU9ccH56QKviFv/MzfPwTL0XgRilCEFRVQ9EzmDSJ16KVaCMJwdA0TbewKaDl/PwcIaIOdDAYRGnV+Tmz2QwpNCI4ysWSo6MT2tZG2qKMlEStY5GihdwYN5tVxXA4ZDAYbORr4LE2gj2RqphtYuDjRadZLBYd2yr+HU3TEsgeu5jqQwt4/uyL8NGLSOkAxCJHyqh57vX6NLaHEI6z0xOksiSJIDOaRduwnJ/TNHGj7IVi2dSslkts05IVOVlSkOV9LJL7947xSIbDIS98/CWEiKBKohPKuuLw5JREZyAFzscQY9tYXKpQFYSmJktSkkLxN37m5zk6OuK111/h7bdej/RQGxfIz/21n+La1RuoZMBsVhHMkLEyuBCoO8DS6IxeMWQoPP3+gOA81rUMBgNGg6irlxIe3D/i7Oy7bO/usbe3g84Mq2UZN0QiQeou8rKxVOWKpq1omhWxidmwKqc0TUVVr2LKjA9ok5N1MZ0EBcIglMA5C6QkWdww4qN/ggh0gvO/gonzQx4q11Rpi9EZ2+Ntzu5OCeahwSp0xbUx+DRlVTcobbh05YDv/PF32Ls84GR2n93RBO8ErfOoznfPuoDzAYegl+dYp3j/zVc4uHSVvpD08wKhFGXVYK1luVxRlQsGI0PbelrXoIQnBEcI0T+h0TXDyYDRtW1Mv2Cpa/pZQd7TSK0wRnF+dkJqNMN+n6KX8eB0RpJlVOUsUvwzjTFwejRj/8oe07MHrKY1ufS8+e5tsnzJ1UsFRRIBnND46L+UKPqZoZ+nZHkgGRjkKCfXY1S9JGmOEAq2trYjY2JekSYakyqES3j73XPu3qs5SaZ8am/M8hR8PuGlGz/FcvZ18qzH4UKi8j5BB5qwZDGdci1t2dubkBhH0Dn33n/AfD5ntLPF0kiu7V/nO298l2Iw5OjonJ5SJEowTBK0lyQhMNKSYtLnvJG07pR8ZHhwfMJs1ZLrFSo1zKeO7a3dCCLhGRUF6cn5Y52Pvg2IlUQXMfNeSEGyjrpNJUIb8n4P5hUojzKGZmlpKovpZQhvEaFGC4kWkrPDE3p7ffCQjVLOzk4QBkbFkDZIBmaLp3c/idSGYnSZUX6ADppc5YyyXbQ0zJo7HM9vkfqn0f3A6eIWi9U9cnGAlh2okunOcD5wEaV/uGZ/b7F1Ub++XvdbZzdAlfOeLNUbb7fVasVgMEBKaNuaNE04Po66/L3RLlJr8mwQjUptQMkJk1FGW9Voec7lawM+/3M9/tH/cErZHPD3fhnuvPMOh3dv8e/88vP8r/94ysq+zMkfFWxtW5LiHtr9XX723/sKX/xv4fxeyjf/cMWP/503KIa/RDqA5q5h12xhXc63vxyoxg3s3Ea7S2xd2uXVe2/y3LXrLKv3+finr3Pr9luotGJgJrz73m2KcYLPHM403L17ypMfe47bd26TpIYXX9jn9p0H/NIv/gN+8zd/k5s3rkYWzGMca0BjDUAIoRC+xYULmzUX8KFFytgfBhDr5JgAqU5pbU2mNZUUtOWCwe41jO6zawx7/afZLj7GMNulblb4IPHVNAIHuodP+0gfWeAizVHERiRKbUywYwSzZB2IFoJECLsBb/Bxk5OYLMpatabI+10dQNcwcxtGRlMtqVYlMrTk/QnlakGqc979kz9g//nnSYtJDAPIMzyORPRjBDktWdrHpAlGP2SmSQFCP/T+8C4mXDkBRsmY2gMEERCpQRFBKZlk0XOx25B5wPk2Sha0IAGESLFtG83MUbjyDCk9JAlpOiEkCfkg8Ivb/xVffuWLnJXvU5Yn3HnwOnujjPmgpT9t6acJlRNkmaIVIFUgBIf3kgiIGNZpfZ7H6xWmjeL601coayh6OddvXGPpW157/w5379+LmKIEQbpJxQvO40NAGwXOYRsPvurWnA/WkheBh7U0aM00WTPY1+yTR4dzjrKsOTk54d7t2ygJqgM1bBMbutG/ReFdS5qmYCA4h1GauilxtiE4G5s1D05omkCqDYf373J8fIxH8cmkR5L1SXoK1/mCRWKaBC/wvu2AAbkpvaSIoInwAW8dvrWsljVvvXWHd2/f5zOf+UkOnrjB1779dX71V3+F6fycRbNEJqJLLwrYpgVvgTYyg3m4XislO5uG2IRfN69jQyu+r9a7uPqLWE/S+cBYGzg+PmZW1ZyczbABmu6xadPgvcO2LYSUh0lUES+2jedgu8+Ng2v8xI//ON++8+CHO+F+2MN3zUG+V061eYj3H6lmfghsRel1IB47a2NgT/SpFNS2JRd6o9g4CIKjs1OqqmJre4zRIoZi2IZRL0epKCUUIjbG189TFAWj0QhvHYNhztFhn+npNKpIyiqqdIQnz3OC6JqVStM4S4PFCksIglaC69Rxznl64yFpVjBMEp6/+Qy9rMfv/87v8rWv/BHj7S0a6yiXFamSKP/wWpUdgPhXIaD6MCbORSDn4tcXZYQXnuAHep2PKNYXBCnwwjEeD/nkJ15kb/8SAUdVVWizpuTJaELpK5IkI0k0iTYsVwvsfYsQajMZqqpiPp93Mos+OE9d1xwfH7NaLdjb20PraExo2xawXNk/AMAHwWqxojWdJng4RGvNbLViMh4hhGB2ZvFNjcBjNFT1ildeuYtShp3dfVar6DMzGsWYvq2dLSaTCYNBr1tYl13MZUDnimeefZZBf8Td+0fcuXtI1TRIUdG6QNN4THejtLahbSKrZndnxAsv3uTnfv5vYsy6a87mhm+DZ3V+jrWOLC1w3jKfz6Kxo/fYJKGtG6qmBh9iypZS1KuaxXzF9HxOnhaUZR3Nk9c0c6EiFdI38SbtWtrWdTGIa516NK7Nsoy6LhFCkOc5xqQbWca6S9Dv99Fac+/eg65jrbrniZu68GiE3WMYjxbw3x+x/vBrIsh4s9VCoxOF9YI0T0lWKWUzp6lqrKuYDEfsbI8xZsHi/Iw8HSGTlLIsOb07o65bjk8OqauW4XDIMzeeJwwUo8kOnsBkMiGIhLOzM1obWWlNY6nbQD8bkBY5UhsSBYmSBBtYLRt867FtQElJ1h/yhb/5C1x/8gm++MWGW++8DVIwGW+hTI+qaXn11e+ipeH5TyjSPMevLEIZsiwaeKZpinMtWRaNxxKRMx4pZGcS6r3HIqhqx3deeY033hA8ceOAq1eeJEkS6tpSLZfYTufdtBW2rpiX5yjh0UrgvcW6CCpok2EyRZL1SXtDiiK74Pu0YrVadecnQHAR9Llwbh93gsVfZHgPV7af5F/+L6/y0o8pnrx5nUYtWSxPEHgUjiAE1nr6ecLMneDJ2B08wdnle7Te4mrJfHU7yk3VE+g2pvP5YLGNY1gMqFdnLFczXvzYc/zOV36Hz3z60zElYqERUvHNd9/jsrxKZQMLE8h7WZTGKIUKYJ2hP7qCDoHx5W3kYEJQKcYHvDEoJOVqxqpcYIwiK3r0i5TcGIZZHc95bWiqhINrGWdHDVevT1g175AYxbLROHuXZ27sMJ06RoNtwLNozimrEfVyRT9LuXJ5jDaBwZ5BOMnpO+/TjA8pQku2l9PrFzTOQlZg8iF2NUehaL3ntPS8N50j8oYrLz7Ht8+/ys+lOyxWR5SiwLmGKgkkQ8UodYyLferxgLrJyJMJcpKhXYPMMu60Z5ytznju2at8++V32d8dI2RLpYe89/4pfQE973HJgKpcIPselS95YjyiFRIvTtkzfXafMBRFigiBRGU0gFtMGQ/67PZzQni8m5WqrFiczZGiwAVL2l+RypR+aqiqFcqDzgJNaPE2EKTApBahWvrFNpaa8/mCTFqMACth3kqMFJR+yJUbT1Kk+zxx9ZMQLIkecak4iMCsdJwv79O0sDXaIVGCul4gnKSfH2CriqcvPcMkGVAPb7Bo5hRdCiW6A2+7jSZwAbcRcQ5bh0k0ZblEqbxjzMTiaJ1u0e/3efvOW9y88SKKnKqcAysS+dkAACAASURBVGCbFiXipsG1NdJHBtLOzh5t2/Ktd15Ga0NiCvI8pww1eZrg2jk6U9w/fZ9aGYbjH+fZpxWHd3L+6X/3e/zn//jv8s/+yWs887cb/v4//ALf/s7/y9H7u+ST+/SN4vTuA5bvTRjeaLh//w4o2H9yRJjn3H3wFp/9wr/L0eJlwoNneerFByyb25TVnHB6DbntMJnjaFGztb3Pa19/ledvfIqXv/V1jqtjfuwnX6D2p7jTHVy9z+4lyde+8SqIGlNO+PzBv88fz36bf/4b/xP/wX/4y/zzf/q/b5glj2eImO0cYlPJdVL8mKDSAYvCx8d5cHiEcN3av/YPUDQu3iecjabVqhhQtQ1L0XBp+/O0fsnR8j79bA+VTrAhJkqFoOOGz7toaBoCvq0BiVcKJTvfQQHr3c9Fz7xYT3SbWg0iyMjSIbKZbOiYFkTms5QSo0T0hxSK0dYO5WJG1tthLOK9cXj9BYbjcTw6IqYXiq4bLIGuGxJlWlptvBElYgN8rYGxEEIH1nhkB5R5H5AystuzLEpKpO6kQR2TyOjoCbHexFgfj7lMCtrWIoeXaQgkwtCGOho1S4VKBJ//5D/AeseXfu+/wcgKoTSBwPlYsn/q+eab99m5OiAJGiE0AUHrW0SQBDxeRkNrJI/VR1FJGBlL7gXVYsbsdMX51DNremhVYduKBGhFE4G97j0KwPl1mhG0jyxOj3pbrD+vGSVrgOL7ATgAQgbeeft1VF2S2BYhozdInhpk6nBJTbolkLIA1cdJFRlruYO2wSjBwDmaVWQSXd6asFwumU/P4uOE5e3XXgbfsrO/z5M3n8GES5GBZl1MZZOA7BhBUtJ2tfXAxWt0VS0JMlAFyyu3b/H6rQc8feMFnnvxs/zqr/wav/XHv87dk/vx9x3Qds7P6+tIxjov+A44lSEyr/GMBz36WcrecITynqqqeO3+KU3dIIUgCRebtmJzX5gGUI0gWTQEZQjWolQ8/uWqRmsNJNQmRptLpaANJFIw7BX8/Oc/z9PXLvPMlUsU+eNlKH6U0eKRWj4ECAmbY7Bm02yCtn5AkF5e+H0hAogQzcdDlIEqJTZ7P2PSKPPMIbEN4+0xs+WMs+MjBv0cJRNa60mTgnJVs9NL6G+NMEmGlgqvFFoZkjwh7xUIoRjubnHpygHL6Sym1nqL60JzqiomW7dlE1UzzQppIRE9KrdEWE8v7TFdrhBCkWQpo9GI61ef4MZTT/Mvfvv/5v/6rX9FLSzz+YLMJMxPzlitKlZtjdcSFTTCg3ctSnhcANkdWy3NhiEaRAQNfXAIJ9ECnBNdSt0FGVSQrO8hHkHQFhH8Rk4fumPt12uAXCc9gwwS+ejS8APepj9Sn1uIiBj184LJzja7u7uMhv34pl2M9A5hbbZrOp8UASFS/9M0Iq3BOqpqRdNUJEqSJBpjFFJH+t35+TnHx4csl8sNxa6pS+bzaTwJqwVNEyUaTV1tTJWllBijybOUoihIEo0j0v6imfA1Ll2K7J9gHdZbhJI4FxeNuq4v/K2dGWlHK8uyjDTJo1N8nnfx3gYJ1K3Dt34DyjgXHbi9t/T6KR974Tk++alPsL21QzQjlhs6LsS4cqXExnzRWktto5zp/Pyc8/PzTRx7CIHVqmKxWEVfnDrGfJ+en0WTqBBQKh5/YwxaSaRc08YVujMnVkoiQuxGlmUXhdvR5Xq9AaPRiH6//0E5kowFlVKKtm271++6BM49XiZO+Gi0tw8FcLobbfz6oiayK6BkPI4ESW1bstQw6BeMRwOKPCNVGltbXGsBGePytCEEQZ4X9PsDkix6TzRNw1tvvcXpWTeHq9htyNIclRik1IRYbUXjQqFprae2Lauq5ny65Ohkymw6xwXBcDi88H4Fy9mc09NTptMpq7JkPp9zdHTCtGOgVVVDvaopy5rlstzMnapaUdc1zj6UFfTzgp3dLba2tpBScnR0wvHxCdMObKW7STrfxrnuWrxtOrnecgMiZiYjz3oxZjDtRfOz9bw0UVu+WMw5OzujaRqU1nj5EK1ea0jX59nzMDL4R2lolXD8oKEYaH7uF34GIWtaF131kbHwFgp6vRRrK4KQDAYDbLOkDVFvDp6mqlkt5lTNKbPFPZxfsFxOccHiZcC5SI03aUorF9w5ehvPkvGox85owqu332DWrvDeYoREFdGkTmqD9QJlkthlaVuUTKmbFXW1oClbfBBIkxFkAkKQaIVW8RxprTfAm0kkzjXkhSHLDc7XWOs4PT3HdnLYK9e26A0SsjxnvDWhSFLGgxwlOllsogkqYPIclGRVTnF2iVF2c80pFG3TkGkDKLzUtKFLT0kMV9KENlUo6VCqxVcrWjQLGwheRy+MoOj3e7FrqTXD8YSm6SS7weKCJSkSHA5rFUYnGKV56tpVEiCTYIRlq0iZZIoil6SZQGWSVkmqAPeO72EE5EIhXUBkEVTY2ooeQrP5gsdtFRYIeBWL2KpjaEX6vyQEgXUB6xxeRfZB6yweSZASaSQ6TXDd/cKYnGJs0JkkmASZbDPIn6KfXWeYP8F48CRKSNJCobTApAOqOkb0Or/ChorWLkF4MtXJpoKLHh1Ckidmc49fX/Mfqg+/+PdtNrFhI2FYr11rv7bNGo4F37KcTdESsiTB1rZ7Tb2ROxid8NnPfo5eb8DR0dHGs6G2C4IwnE9XpGlGlllODi2f/omnyXJFZoasTs74hb/3cU7Oam7d+yaf+MRnQZ5gmbGqltx+22KM4blPZrStBwVtEyiGEilz5tNb7Aw/w1Mfb9i5foWDZ18gLwxpMSAvBrR1haqgnq24dO0Ki3LF/ffvcPPGiCv7Ax7cPqUo9jk6LZEWnrq8TyEVRiVMl6e88NzzuFXFrTfeZbs/jkafj3EIEcEDL8CtE2ou1BWPsmk3chWx7lTKCz9vcTTUYUW5vMO13b9FxRKdXuNg+8cJukDpAqX7CJlEHw/hNgX0BagE+GAKycXPzrlNfeC9J3SskU1dKOWG9r/+P0CXrI0QYiM9yPIeWa+HkIq06NEbDDbzd/086+eQWhGkQBm9mdebjdta3hU+PPVkLQFad7211tj2IegDdIk/ktb67h4Oa7OTjfRHK0TwmBAQWNAGlMQrEQ3HZcDh+Pxf/88YFHtU5WlM4AuO26bhOZFR325Rbi2nWedQrU08usab949ZaiqiVC1ITk9Pee+929x6733quu5kdPDnLD0/8FhP6SzLOiAt2hBcBHw+8M5ETJs9Pz9/WE8/wjRcj/WcQcnNvXn9vTzPKYpiw9ZfJ2V576nLilu3bvHWW2/x4MED6nJJW5d424Btu/MR36NvLcJ7FNEHta7aqL6o4fRsyenZnL39fV548UXuH93j9//wd3nnnXc27zfwiBH9hT9hDdwqEQFIKR+u+2makqbppk7cSKH8hycEBYjeWoSYaPTIWrIxlQ4XXtvHa2YyGvP0k09y7cpVrhwckCQ/uiBOEGLzgfwg2Az8QM3OR73jYmLVxTWkAy2IrG3XWWZ4HITIxtFak6YpWgryPCPNEhaLBc5HoEebFKUM2MieTIj3WUVAqlgPN97RtC1BSbIip+gX9Ho5k60txqMJ/d6Afm9AZhL6/T79wYDEpGQ6R5GhREKiMhKTI4SMQKTUpN3jJYIv/9FXWYQaT0AnOvo7uXhdCSVBaax4CLSKAHot7/RhcyyQF8D8IOLx6VQbm3PQiRxDZ4Ip6CSwQnZADd/z8YHRMYL+ouMje+LoxPCJH/sUTz55nUv7uwgUrVjhXEtZLukN+iSpRmmBED2SVEe01QaMiWjZSX2EbRyzs/PNDWdQ9PDCM2/ixnK9EZxOp2Rpuim4wHN6OosmxzohyQukjEkMazbPZDyOKVfTGXW5pCgKjFQMix79LEc+r6mdxaiEoiiYbO12f5+PRr7OMZvNaZqG7e1tijw6eIsAp0IxrFvGkwm8e4u6tUjR+SJIu/GS8aHm0z/2cV544SVeePG5mOSSG6raRH8gXwFxETZK0u9tdzF3LboWmFxycv8Y5xzz+Zz5fE4IgX7R29zwV3UEcpbLkradobX5QFGgugtehOgRkOc56UCjdQI+UFZLXNlxSPGd900bk7qShNPTSJcrigKdmE6vu2C5WqGMpm0tdVsinaRuYgTj4x6Peif8ef+/+HtxkZdRFx4CeNcZGRuyLM6rdtggpcLbEnxDZjxb2z0W84blMm6CiyJDJz2evnEdKTRlWWNSgw+B+WKFwzGdz1hN51y+eo3MaEaDAUXRR4WEso437KpZMTtbxM6aE5xNl+ACuck4Pz/n9OSQN197hbJaoUyGyXK8c1gRoqeU86RpjzRNOH3wgNpZBkUPaaKZ3Nqoz9omgn3dMUm0Is0SklXCcDjczLPPfu4zNE3Du/ff5cHRIdlswcHBVRAKryy+VThnaX2L913sfLMiS3qkSY+0yCjyPlpHk2WkpG39JhZ4Nltw985t6rpm0M/Y290lTZKoafaeIARNY2OxbMTGx+LfykT7M4ZRhv/6v/g1vvR7/zOVPaHhBEd0xkzSDNsGTBqYV8coVfHguOFnf+aneO3lb1D7Kl6fBAqT4RpHaY9pW4/JSk7OT9k/2OXB0V1uXH0K+8Dz3u1bXNr6Cb71py/z6ZcmnJzcppfmHFzf5RvfeZm9QcG10GNWrWhVTMDLeglBaN679SaTcZ+mukzSX6G0YZDdpGlnVCEj6/UwqkeuHFpJUiOj2XmikUGivef87D5Xr25zevgmQkR6fGIyhpf7HL/7NjdfepFiS+FsSzoYsTNZoXyDYYx2gdGkz6Kas6obpG0ZDRR7ezmFAlSGDwonJMHHeS10Ri2hFJZ+YXj28pgrYs53lue8tLfDC9d20GXLSihOnKc6XzJIU9hWrFYLZJow3N7n7fffZ+fyJe68/l2saGmpqbHcPz/h7tGSzPR56touX3v7Xbw39PoFVtccnhzjK8svPnETEsvp0qGanIE0FP0ZqVKIxqFE4Oz0HqHyWA9JkrB97Qmse8xaexWwqmGxqjGZQXrHfL4k0yPQhtp5Qt3gwypKnVfRNw3nGeQ1MlUIYWicRntDb/wUtlWkRrMMMxq//P+pe/NYy5L7vu9Tdeqsd79v7b179p7pmeEMh6S4SLQWW1HiREYiObaF/OEAiWEYQYDYiB04CRQH8B+OkASO9yCxIdlwEimJLEqyRCqUGIm7ZhE5a8/0Nj3db3/vbmc/VZU/6tzbPdTQypDShCmg0f364d13b51zqur3/X0XhBHMF8f0k00SNeJwegfT+PT7m8zzYzY2NnjrnesMR32UUASyR+L32Slf5mSyj7GaPF+gqahFSBTGjg1gwf82ldT9BfRy/W6aZsWAACd7rnRFFPrEgWCyd4jRBcZAVZQQCCSKqsrxpE8YRWjjooDTRc1ouMH62jYvvPACSZKwfWpEViguXHyI/+vLv0lRHTIYe7x902O4GfKf/Gc/yAtffpPgwZf4yEf+Ao8+/jI/9w8/y5/8s8/w879wxI/+uxf4zD94ix/7yYRHnk340mennH404uq1d7h09htE0RlGm8e8/I2Sc52f5Ol/7TP8zotf5ic+9K9z/c0Fh+UucSdmGHVpTE1nFDLfX/Df/fd/i//6Z/46TX6NeuaRdeeojkbHaxwu3mC0MWBvXnHz4Pc4G0seuvgAr770Ev/Oj/44X/mXP/tB3o0YNFo6UNDTrqMprRPTLOVzy31aWqho8LTA9xTGGoSoV9dfCEGlFP3eQ1xKnqEo9xl1HuRS/zQEHkIkGDSeEDR22ZWWNKZemctq6wrhMAzd9wXQFkRG1zT6HsPCSdndAV6b5l5RLYwzPUYjWfqegLUKqSRJGCCNS/uMOx0XgJEkq6JyCTa6+9iszh/g4XsCX/lI5d3nPShBG5oWtFwWH0tmgjUCId9tEquUwuhlsXfPd8QlBjWtL6P7rMpbRku7VEQjIPAU2lRYPGrtvI0a61jBoVQEg5CPPftXEMryW1/920ym15zP2wOGwDZ4E40eKoxtMAiEdK8tjINzGufE8QHdhdBoQ2UE+4dHXLtxm8PDYybzGdq4hkIQBDT14jt+/fsBN9FKQTqdDta6+HCt3f+/1/FFBZ6zVGhBtiBw+6rv+86bzBElHKNJ2Pa+dUCdMBpan6beYIRtNFacoJTEE9vs7++SZW6vT+cz5mnKZDrnwYf3SJKE8xcvOfZ9HKOiGCEVQRi1YSY1aduonUxT3r6zzywriXun+LEf/7c5Ojnmr//Vv8Y3bjxPWi0cU6lpnH9VXf0+7yAhhAM6JSsPRE94WG3I04ypp/CFoMhzSt1QW9dcNIL3buD5Eqs8GuGMsoXkHtPB2pWZva+Ua7YaQ+RLzm5u8fCli5zd3OT0xiaPP/rIqon+vTjey0vlWwHw9ztkS2paMt7vn19pnfEvOLN2zb1aKlA+Vjt7gNHaGjdu3ABgNBoxm80YdWNKUxEJSWMMnq5QQYw1LUGhrDHWEtU1vlIIJKrd842A2miCOKI8PiLynXXDUm6Up1M8oUh6XXKt6fd7DMcjzp4/y7mzF/jQU0/xX/4XP80bb72J33EWLI4d6c4Ih4eHhH5AUeQulUyF1HWFVaolBgiklTisVeK2hza0R7TGPMJlprlxj00q5fLc4gD/pQfYe16hP8Qa5n1nn3a7CZvbG2xtrDPodalrjTVOi6k9QV02KFWvUGSjwZhq1THr9ZwZcVEUTCaTFQNkCT6EyrFohBBMZyfMpgrb69HrddriTxMqByg0JidMOnieR55lrstqanrdLmVZMk8XCOFYC1VecXI8R8Ue/d6QtHBO5MPhkE6n026Mjsa1NDuM4/hd701Jj263S1U5yjZIjNHtBQRfKfLSeYT0el2eePIpnrj8GGGkMKZBCLeopOkcFfgkiWMxNU1DluVEJmoNS0N861Em+er7WmvKsnSx6x3Hajg6OqAsm5am6bpHWVEQ+j6hH+EFAdaalcbU0TrbDo90yKvWFimdd5HrbN675YQQhGFIFEX4YYAxhsXCucEvKaJFXoPQGNt8wF2V9zeW1/T+IYRwXkLCYmiQwj0Ovu8TRKHbUJWHr0JqU5KnGXWTkU4zqgowNYGvENYQxAFBFBG1rJpFviAO3SJS1C6yfn28Rqfbh8Yd4qRUlHXDPM3Qjesqz6apA0tFwHyeoYRsQZGYptb4UUxT1avNyQK6qijLHDxJUxbMrCXqdNgItlG+pCxdipkxhjTNVhRf0yLQDZZm0ZBJ9wzFcYxSksZokJLTm2c48aeIVhLU1BptGrSu7rFmPOW0zTIhjhJ3H4sA349bo1QP0x4krTFUTc3h/l57qK25cfMajz/xCGtrG+1Bu3FG0fd1opaA7/faWMyn/C//0z/lKy/8Fo8+9gAGx8zSukbKDkI0SA/yYkESa7LSFRTz2SFC+qAN2kikF2C9GiE0UeTTNCVCaMCQZQs8P0B6PnmeM59IqtylXUWxxA8sl9bGvJreRgWaoiqpGjjKThjGHkESMptO6SYxcSwIfcVwKCnqgrzI6MQ1pRYQKBASjTPFlGLZoXVhqoiGsigJgw3nYWENQmqGoz5NluL5BhmHjLZOM5tM8KOEMImoDmYI3+L7HkkcYmyJUB6eAYnBCxTSE4ggpJECWkC6bhp8pcjqmkZCrx+itOZ0Z4O3heSB3jpbcUyeFuB1ULGP3Z9hshRvI0GIgCCMEH5IPpkhPJ/pbI7yPQajPnlxjFAChE/TVMSJYr2fEIg5SljCyENVHkJIynzOIEnoJgFZ0xAJxVocoQKPqjEESjJUEX7kzLtF24Hy3sME849yeEoiQoEuDKGUFEXqCsCW1tsY7ZJ2hDORTcIIqyV13eBrQFskHnVjyIuGPIUokHi+ZDo7YmzPEQQ1RbGPLmrW+uvkzQKsz/FsQZof0KsCZtURJs2J4w4d7RH7PQgNs2JC4ocs8hMaSqJwg/A+VqTU780k/lbGxpJd+q0mxbQeUI2uKfMMP1CEQeDWrqZxpq24Lm9V1e46eQIhJOPxGjdv3uTJJ59kOp0ipGD/6C5r2+uUVcWTl5/lSy+9yBNPfoKXX/kGnbVjDk6mfPL0NvPpjKu3Xubx584zPjPA8xVZfkw0LNhYf4j5fE6nH3Du7Jgm3KAxU0y5QVYt6Hf6KDlHlFuEwZR+v4PX2afOJ/SjDvuTI6b5EclWl6ef/Ti/9NnPsFiANTPObG9xfecmFy8/xMFxTrfXwzQ1+4dTHnniKQa2Jk8PObx+iEocGPFBjuUeYUyD44lpB5rIpZTKjaWUdllcvKtAEXYFOGz3nkBFG1zf+yWeOvun2IzWEKqDkDFIUO0+hdat5MgglNea67pid3neWwKCS3Yx3CuOliDH/ea0y3vs/g62bBnKtPek73vY2nnpLBnpSwDJRYy7z9YYg9BmVTwJ0aa/ag8pNI10548lle9+durya8fYoP0c7nWW7/fec2FX7/seK8+ZmN//uu5zu7ny2u8pL6TGoKxoCxtntqyEYDov2N44z/7hLkmyjhYLDg6v4UkfqUKqfE4z7iKbeyyg9tK6uRIfNCNMIKTEtNdluYY4U3MXcd+Uq3f4HY37WcSq9S+6Fzbw7Wu3+yPI7wc+vvXMs3x9jXVSDQFWOoBMej5KaYx06gYIkHRW4GFe1cjW8mI2mXL7+pvESReMIe50CDs9uoMxQnmEUUKt3Zk0L2qKomJn94iTRU1vMOa5j/0Ai7zgy88/zytvvULWTJHKMc4t1rG77gNexPLDS4EncQyK9m9PgK5LCgtpK/3O84yqcXbosiXjvReIszTlFQDKgRHG2hXXTuBACE9IdPt1v9fj0rmznNpYR1iL8gShH31XgMgf9bj/Gb1/Dfhuh7UWY13DtH1K27v/3XHYUspVtPvyz3IdTZKEw8NDyrKk0+tyMinpBhKZpaufjVomeLxcj42FBrIipSmLlYKnrmvyReoaty0LbBmSpGuNRCB91yDLsowwjhgMelRNwbnzZ7izs8PvvfKiS5xsDcnrusaPui3gaal1w+TkBICt8Sa6JTpY8W7fGseyuSeDWgL9LdfZzQ0WVuuYab203Fga5X+7ef/DqmXeH4hjLUmvy/p4bSW1kRJ0XTCZz/GEYm0LosQZ4TbGUFYOiPAEyJaa1OsmhEGAwMl56rpmOp0zm80wxtDtdFwsmR8hpKUsXdrV5uZme9jyyE+OyZsCbRRhFKCrGj8KmSvFvDdHShfxV5c+tRE0NuPaO9co84LBcMz29jYqDLh79y5BeEKSJIzHa85tW0oMwpmyaYNpixltDf3hgCCIePrZp/H8mGvXrvPa61ex1hJan7Kq2NgY8+d+6k/zzDMfIo4C5vMps9mM6zfeQilFf9Cl2xtQVQ2m0RwfH7eLNVRVS4uMfMI4QiqPbq9HHMccHR21VC/hGDizlDRdIIRcJU4IKxGIVmY1QQUKIWQLVEE6T5meTJGebYEEt2n0er3VYr/8//F4SBQ7amNZ1dy+vcvJ8RQhvBXYNRi4VDKjzXubM/1/MN7rAbm/QwX3FkVp3GNnLGhZYlFEcdchsiIn8KZk5MwWGcLkCGnRpqApKxoNa4OEIHF+NPP5jGlTAR7DwRjhaV58+UVCP+T06VNsb59GN+7g0DQwnaVUpWExL8nLmtFoHVs7uVWnI/GMIstTdH2MMJbZbMbkZMZ8dkg+X7Q+Rk5Ot793h7DTx/d9Bt0ex8cB6xsbDL0BTVMx6A3R1qHnUZIQqpAin7cHG+f6nucl+/v7DIfOW0r4qk2QCpBSUTWaerZYzbE2DXmWIwR0Oj03r9qxMpTnOVqh6qCEQ/XjyMmhZrMJebYgz3O21zcoypLnv/5VDnf2eO6jH+FHfuRHqLU7RKvgHnUUKd61SH6vDCksP/ETP8k/++x/zO07bxNFEXmRs8gz4s6AxmoaWyFkQ1bNUOEar7/+KmW1IA430FZT1TAtNbPMpedsbPQ5OjpE+ZKbt64hUM7DKIhYpCmPP7JBUxqOj9/E9yO8LOFy0uf5i6eompJFkTNI+iTbMelkggjW6CWKbtiH2DCbH7OVxJSzKUbnXOgHLPyYnbQhrzRerBC+cf5GjaFuGvAUsWepqgxtCpJIMJ9ldLoBvo5Y1Cc8/Mh5ZjWozoBRPCIOJPqdm8jIQ+eaYX/M5OSYxeyYrc4pfM+jk7jDo8Ji8BGhh64MTaORcch8OueoyJCBz9nzG5jOlH7okU1rfnD9NBc7XW4UC/qjiLP+NnuHEzqiYpB0iJMQFXY4ms156NHHuHX7JgA3376B7fd55IlHufr6NylNw+bGiNdff4Hx6XP8yFNnOHN6yN5ij3e+ss+59YDNhy5Qi4wbr79Jd7DJpJrhzxbQ7XOUTvEXBR994EGSvs/hNGWeV0yP9v5AedAf9vADhQzcs5MkXYpsH98LaRpNpRuKosSECYHOmKcZa70NIGA+nREEIWXZIBGoIMEKRbcrCFVIGMYIscXjl/4MRh5SZDeI/JjQb9ifpXjKcmfnJYoqpdMPOOENrt0+odcZEJbrXNx6hElzxDt3bnBmY4ujgx2iro89qtnc8IijHkpKZOhj30PyszQKpW2GOPq9WhWmK2NZSqaTExbTCUkcIEhcl00XlLUDsa30EULiRyHK8zAGpBGURU0cdaiqiuFwiMGgYs0/+/l/iBJ9tsaP8dyzXX71qz/D9vlP8I1vfpYnPznmTPcC/+AffYY/+xd+lMaccO31Y/7Mf3Ceb/5exk/9xYTF2+e5c3TAQ4/18OSM27sayxGPXfhBjhef48nHP8KGOMPe/OskB38e9Zhh6wFBqGNuvDoh2RiAJ3jo/DZX3/wmg80RD105Ry8JufnGy8T+iP23FwzXLxCjiIXi1s1d9q+/zXFHcpjt8X0/9Am++PxvEkTvu3f3XQ2XfGKQWGrbsKSkW5wH2tKixwgH4Gis8z9a+rQAWAfYNVKQ52+BOeGPP/mfcpTOsTKGvAxrWwAAIABJREFUKEFiW/ZAC3pYx6JZesEspebvluq6w3hZN7QlH1bXbaStRutlWqJcJe0smc73PE9alnjbKAPQnoS2MFkyM4SSdLwIa2l9+ARGSkRjMWiEp+hEMcJa0qpANZI4jJzXU8t0XTZHl405uB/QvK+AaMGp+xk7xhisNk6+qxzrQFpnDCrlfQweIUHd+3kPELItXqyLwxbC+bXYCkaDEf/Gp/4q03yXf/yL/x5K+BhtCYWgtgItXIqWtQ0YF1/dWIvQ8gPdzaXnIX2ffr/Pxobm7ZtfZmdvilL9FuCtsFa6iPvvYNwP4CyZXnVdUxQFda0JArWa09/3swKm8xm9MObBU9uURe4MlaWH1xaZwjpJiud7aHRblzgbBoNA0YIYWjNaG1NXBccH+4xHQ0bjIe/c3SErnSepLySzw32mdo8b199CG0mjQmrtIX3FYDhiNBrh+z45IdYKhqNTPHrlo1y89BBBv8t/83d+hl//3K/w+s1XiAY+1cJJRZXv05Q1QehTVQ4YdVWJe39R6NYfYR1wKzD4UqFkazNRllSrxrPzMKtrvXqO3jVv2tLoFnhceqtZZ08icOf8FRBmIVCSS2fO8v3f9xGkMaAbFDCfzj5wmel3M75VafB+f3Ylq+JbpD7WIO9j+DigwsPzDD5QNeXKNqTX67HIMs6cOsVrb7xBlmU8NhqRp1OMFhjPc1JCa5EqhMalp0Z+TBCFaG2ZT+ak8wXGNC0zsgUOmwKJx8nJCYtFRpo6TzQlXcBSmk1Js4wzm+tkdclTj36Yz/zaL/Mbn/s8MvTQdY30QnwZkLT2KtZauoME09T87te+htWaTz77MYZr61RW43sSrEdtHSv5vUBDIcDxkto9VGjkKmWvlYrTStf/gOtwv4/RdzPe927e63UYDPqEoU+ezinTjMPDA9I0JUp8xnodiVtYmhagcb9J0GjtUmvqxi1sRU1a5JRZznw2Y7aYY4yLCLPWEkURg16Put28wjCkrAtmkxnGWJSXIKTbEONOwnDozIn39vY4e/YsUeRRFRnHx8dMphPydNEijw033r7hFgxtGA2GLOYRx5MjtjdP4YeBY8uEAZtr62RpAcCg30fXznh5bW2NK09fZjQaschSZtMFp85u8dQzVzh3+gyPPfoQeZE6g0KrCSKfsnLdoG6vR6fTYWfnOr6nGK9tcHd3h7TIiYMQ3/ecIarnkecl165dW6VIGdMwn6dkWcZikVFVzptmMpmsrMajKAJoY6VL92DUDX7oEyifIFStZCggjkOiKGE47BOG4QqIcdRPx0/UjSFNc+azlPl87hDQVrcahqHr9jT6e0bm8l4I53sBOHBPuywtaCGdflsGyNAQWgjDmChOCJUDTDwhGAxGxFFDWdfs7U/ZvX2AkJLCNpSLguOjCeAYXx/+8Ec4f/EBkqRDURQcHhxTFBVVqZ3ELRzT762BragLF1HcVJYq1+jGUuQNi3SCrSum09b3ZjbF73R4cGsMxpDNF5RNQ5YtuHnzOsN+wjkpOTw6oKpLhoMRUyaraHilAvr9Dv1uf+U94PsJSWJYLGYAVE3totO1M9+SwsMDsrLExcB6CCno9kYIYVGuEem6f55ySWgmRHoBQkgMNaWuMY3m6OiIuiqIwxBDQxAEnN4+wyuvvMbOzh7D4ZjnnnsO4avVwdXzPKwwGNvgiQ+2CPmDhpCW/+gvfwrbeEi/Yf8kRQqNZ3x0XeIJSGc5i/yI0Wibp89e5qWvP8/B4QGXL4/J8ylBcMLJScpweBbfppwcnhDFEVdvXeWxc5dYZCWdfo+dw12ULzic3eTio6eYm2NMViKaLk15mif75yC6xUzvUFnJc+c2+Pxsh8M644FTF2mCHlARJecJu3tExTrX39ql2nmdR5/+EzTmDEE/QjS3qGWO8QqsHlBWC7RokORIVZCblL1pBibk9EBRZYbu2jpWSo5mBb2eh7BTlB+iLZxdTzgSJSpyxqCb62ucHfsILZhmFccnKYEUjDcCCgWm8RBFzoldsNANnu8M5WTo4W0O2c8lR8dfY/zARa6dTEg8SZ7PGHRj9KUBvrVE0mPW1PQT4RKS5nOSuMva1lluHO4zMxOGwRlsHdP3GjbDDo9/+AozZdkpTzhaOB+IR053eOjiIxwfH6GrHhfWYuIgYiA6HD8+Jj054cFxn15ni4WsuXZtn95wAEFI4ofIOx9sOpU2hrrWdHowTW+zuXUaieR4XjAe9wl0RT2/AeEIX0JdNjz88BX2DmYc13skgYe/mNEbbtMdrePbB+mqIZ4NEV6fZrGDSiRZU3GSXWV/fp1uZ8Te5JvMi2tYscWbO1/gcH6MHwwp64aDgzdAT9CJ5O7RdQ7LEE+A2YM4PkW4GHB+SxH6Eoylk/RYpDN8KciyjOGgTyZqaMBUJSaf0dQLNscP8ttf+h0+8alPoeZwcnRCKVKU7DLP7hLQ5YXXv4rv+5w/c5bA81AIhvGHmaf7CDMhkF1Mrej56yzyCb1eiK9GaFMy7PcZqCv80Kcb/vkv/z32i08S6iFKw+TkKt98/jX+xn/+H/Lrz/+PPPPxmP/5f/gXPPbMOsPNMXt7JUIv+JVfXPCjP5Zydj0nnxzw+ot3efBpSSl7zDtfRtkJk5OUC2fmNFnF+NFjfvfWnOE4ROoRi3yXk7evsb4xZOfoCE8F1GlNsViQzVJkfJo6L4hlwd7uS5zfusRxPoP1mlKGjPoht27d4vkvvcqHn/sQdXX9A7wbrZPamtqBB1ZjcawUYV3h4E4794Yw1vmfSYFnRRs5D6Dp+qdIuueJmwkv7X2df/PxvwSqwmMpsbsnN2pM8y5PNSkdvV/yblbLCtBpu6zGSJqqaj0OTPs3CO1kRgIPrasVULLco97NzpHveu0oiu6xYAwYKZ28w7rGhvIcGJk3DYHy6SUdrNVIz+Po6IggilCtOfHv9/GRKybHkpF2v1eU0VA3lWvMeK3xprkX6bwcS3YvsNpzfd9fzeHyczkz55q8LCiFoRN0WVQTXnzjC/zxj/81fu0Lf4Ows0E17rv5QyKl64BjLI1tmdEfMPHB8zz6nS7xdoCpLJtrA1ItMZnHcVG4zxsmVFn6Hb1+GIarOVqynpbBDZ4nVmdrx/Ayq39LKals5UBD4yRURjcIA34UYuqGMs+x2nnr5VVOEDlfEm2NQyw8hQR8HdCIhjJfoFcNWQf6jQdDoqKhajTTNCP0BFlRkWaaSlvunhyyqCxx0uHRR0f4EaiqwRuM6PaHPP3cx3nmue9DW/hv/+7f4Z/87D8mLWbgwWJe4eG1kkKXcqqbmjBwTLKmculofijpRqFjvpVOwREFIePhiG63i7Wag4MDoiQhKSrKskIYs1SoOHumdv7q2uK1fjrCgDEaj7Y4Rtx7Po1hGWevtaGqMu68c4teHBFT81ZVsHf7NsV9nqjfi+NbGYDfKXPoXRYT9j2+9/vWF8eikp6H78Uk1kk7a6NJgpATrTlz6hTz6YyXX36ZCxfPURQZRaMZDKTzPVI5nahDmlWkpiRQDrCZTI6pioyjowPCMEYFAevjMUkvYeedHdI0pSxLTiYLwkigTUUQdaiNC4u5/OSjRJ0BB5Nj/rf//RfQWpMkXULrEYQJQXvfZHnK+vo6IIlDn9CTvH3nHb7yxf+bT/6xH6E/WEObdh238p5PTbuOt5PjoJm2/lh6kL3LSFqAu1k1rv3wLW1nu3IGW61/323V/L6rIaWc342Uzpm/QYM2COEo2CsjOOvSIspS0ejKdfdaY8G8brCNJq8caBEqlypxNDnBWutoWnjkeU7VbnzWWoqypmiRbTckURTR6fVYW1tjMpm4A99wyHw+XQENUkqiKGA8HlOWpZOLBE5r5xucZ47vowJnpmW1M1cOgoD10diZKNc1RmtMo8mywgEbWQbS0u/3CIKASxfOc+Xy44zHY2diZgWedLRVFfj0BwOa2kVrLhYzF90tVCthCZlnKdPpCUGoqIqylWOolZRKa42Sbl6yzBnSLm8wJ4vV7Xs1BKEzoEJaiqqibgxQo6RaGUCCQ74HgwF+4NIMqqpabUTg5sZTHliJp9ztlmWZA4M6AX4U4iNRUfyBywbez7gf2HlXx4pWEi8lGr+lzEGgQlAWP4qJ6x5xZ4AfSJRnkdKS5SfkrSxmifbHRlBYF8V95sxZBv0RnU5MN0lQQcB8njKdTlksMuKoS5TE7jlRCi+ULIqSwFNEfsBskWFsg/IlTVFRliXT6ZQ8d477o96A4bCLxJD1C+bzKdM0I08L8lJzsHeIF3YpisKBlWiUBCEUoCmbhryp8KXH0j19qdsvqhKv7ZZqrdFCt4fkezRt0C3brYNQLv3D8yRSGnxPIfDxpERJiVsZhDPmrWo8KWmEcDRX6SOVYGtri727O8znc77xjW/wxOUrBN3onsRCtq0VY+GDJTb8gUN4gkuPX2SRZwShh7AetpIEygPrYawmLWbUuiavGx5eH7Gz72JE3QGrQgU+6cGUTneNbsdnns+IwwRqJ3E8d/Y8V69eZWfvHU6f3+T1N6/z4OaDHOcHDAcJxdQwOc6Jwg0IO8zyOQf7b3P2wsfodxRlfcSpxz/B/olPWZ/gRRZPRnieYHK4yzt3rtEbHRJeegCsxfecWaC2DWUxoy7mGFmjVY30DVo3hKGPaXys8hAKlOfkpbpOWSwMkgWJ7ySZxmqCJCaJQnxdk8QRXWkoSs3tvRMGUcjAl+jGIkKJERJjoTGWkprASqIWLFZWkMkSJXHyUOVRZQW1hUZ5eGHQFlyCtfVNqtLJCoqiIIx8jkyDSiKUqLFVQxJ1GA5dR7AbJgi/phj0aJqQomoYjfr0eiFNlaKrxskljEsdiOKAdKKxQmJMw9r6BkncZzKdMUsd3Vd8h53d73QY42QQRjWUpiHsdajTlKTrmiAq8Dg6mLOWDJlOUzqjAUnUocwbmpFmURZ4SUhpSmx2wtnhg/h26IpFMg7SG5iiYWHeYZ4dUVc5a+Mhi+wN6vIQ0xQ03gzDEFEUjHohmWo4Ke5gSChMis5zAt9gGs3Af5Cqbgj8CGtLhLXM5hOiKKAuSqI4ZD6f43kCGovVzvOkLEtm8ylFmXFwvIuvfZdiJA2j0YiDk1tc2Frn2WeeYm9vh6+/8EXKMmdza4OHzjbEcUykRrzzziFx3AGh8JQ78AdBQJYXvPLGVR44/xSvXX+Fj3/kCldf/01Or38CY9w+uLd3wNdf/BLa9jhavMzWxkXefOUG/9aTF3h57ybd8ZDvf/AKYcfjYD8lSrbww7tcfuwsV29fx1cGFTYUeo8bB4KTZpfSWu7e3uNgknL69DaNttSmRErJZHJMtzOkLGpGoxGHh0dkWcn5CxcpigybW6Ikoj+IOXjjDqiURVkwWB/hq4jX33ybP5pA1W83LMYIV9hZ48AaK1kleVhHR1/uyVJKtHaMK2FarzoUCBcYcWnwKDsH3yD0E86PfwClahotMOKe7MnJxJe+BHbF0oJWWuE5MMS9l1aeYi1Guy6qey/LOXIyGwAjvdao3iLuY7cs3/tSsrw8EyyNWdO2eRgEHiCp0bj8bYHRmsVkQjVPCeII5YeUQtLbWGtZP2A8QZ5nhH6ACu75drzXmWYp21qOpefQyk+obBzQwz152HLe3dzr1fnv/n8vwRuWgJNxKac0JTuTPSwVP/Dkn+fzz/8tnrry5/jqF3+W8alTWGPQSx1Vy4Yyq8bZB58+aYzBkxBHIb1OQqQWrmHaqgS+G5nK/RKt++VRy3E/A+J+xo6UEik8mrI95+MYXBhLHAToNjRGCdk2Vp03qPAUfusnYwBhlKM5cQ+cC6OApnaenp6s8AMP4UmCQgEOAGpMQ9UAMmB9c53xxibnLj5MEkVOcjjYYHt7m7PnLyITeOu1W/zmb/0G0+kELxAoqbCqxpYujr0lS2ItKOkhsAivBa2s8+VVUri4MOGSi8+eOk232+XO7o5LLE5TjHGJhB6CTs+BoHVdo0uNEO61jG45JILWIsIZ99vWmNYZIkMYhPd2YWPI85wQw8yT1GXB3FOr1ODvxXG/Ofn941uldt/pcECNA8hYrpmlqzviJCSblA48qx1LMY5jyDKKpiEOQ2xbozJxtXN/GFM1TkaIkhyfTKkSQ+w7xUhta6g1da2ZLRacTFO2t3skSZ/GGNJFCVJROjMuvMCnqF340CCMCZOI4XhMp9unPxzxi7/0GSrdkITuPhGeY/NKzydUPr4KODk5Yr3exlpLns65de0tzjz7LIf7ezRaMFofIKWzKxBWQBsEZK1GC1p1gWbps/atTYAVaA4oBDVLFue3v2ar6/ZdyKveF4gjpANNdKXJ0jlNWWF0TRAopExQKqAoCqqqII5D4m6XRlekac1ikTr/hF5C6Ht4nQ6TyYRZVhLEEUmScGpzC2MMs6mTbCw3QWMMR0dHNEbjeYqmqYmiiNFoxKlTp/A8j+PjY/b29vB9n9OnT2OtoKoquknEmTNnaJqG7a1NivbGVCpAW3Fv0zXOWG6ZchWGMSJQlGXNyWyGaFxaRZGVrmiXiv2dXe7s3KWqKtbX13j8yhOMRiMHOglDkkQ0uqKqCtdd7PRWKVhFUbC2tkFVVWRZxqDfp9MNefXV19nbv7MyXdZtVLPv+xRFga4byqohXTg5irbWebYo5yqvNCgpEVI5+qhn6coYpZzze5y0Hje+TxyHiFbesra2hsXcS6nyXSScsZqyajg6OmI2mSOspN/vuw1JG0LpgK9OJ1lpgL+Xxv1o8v3PyOrhsjW0qL0yHnp54JESP4beYEwSd/ADj+OjXTAlldZYMjyh2NrqosKQuijIq4bt7bOET8Y0GrKyYj7PmZxM6fS7LBYLbt++Tacb8+ADj7TFVkSeZ84Uy2hmabrydnLgkGBv7w5lnlM1BZvbGwz7Iwa9GL9ND6qqivl87iLkq4Y0Tdk/OeH6G29yI7jG3t4OT1x5irW1dTzPwVYegiItqIXz0KjqGt/ziRPnjWSFdFTVpoCsQSrXSVombjitqQMWpZUoFThfds+ivAChBL7n4QnnY9U0kGY5de4AWM8TmMrQ6/bwPIGtGh544AF2D/b57S/8Dutrm1y+fJkHH3sEITTGNE7TLMX3GoZDqSsGp0ccHZ3gF6BTGHdPU4sDjJYoP8B6OyB9rt/Y59z5PcbjiIcfeJTFPKU76PPN117mwYevcOvGTXbvVHz46Uc53D/g9OYGUaz45jdeZPPMBf7YD3+Kv/tP/iYfe/YhrLJEwwQjXRoSm1u88PwxMplx6Ylz3Lp5Bx1bnnriNDfv3CbrVBzdqAgHCaI7IZ1HJIlPVZTYIuZw94C1M3N005CbQ0QcOM04MVLM0LpASx9b5aTVgvGpMYuZxsgG7UWUaYm0BqtTKm3wKFGiIenGHJclIvSJe5YAhbWGX7/tc/3aXaZlxsc6EUnP5+AY1npnIJFk0wWLWpPZCoykK2Jc/SN5af8tnnjkAQ6P9wjWBsz3DkkDSUWDFRZbN3gaVF1j8Rh0OpRVTpWlaKWJNwd4tSKfuGfPlzVX33oNLzzDpcun2DEZBztTTj3Yowoi+h0QIkbaEcIvOTpcEMWC0TAhDDYQVrJ/Z5fsZsZiXtAfrtGLEqIkAPvByky1tgzHa1y9/k3WNtZZzHOG/RhrBbdu3mb71CanTj/E8fw2/W7CIr/DtZvP8+SHznBn/y5V3bC5eZZCexwdnCDKV3jo7IdZLCZcvfMl0qkEqVGdmm7fp1icUJgtDk9eph/7NHlDqY8YrF0hkQ3VcYGu5uzXRywmMZsbI2bzE0wgODh+m9l0zA88+QxlXRAow+7uLoNhl44KKG1N6MeYRhApn7wsVgVKlmUk1Zy377xFMDREKqLT6eFXPpUfcPdgl1NrF8huHCPLmifPXCbqJYS9mOlswa07L/LKG5/nycs/xOWHP00yVsznU85tXeKtt3ao6gXP3/pFPvdbn+eHP/kDlCcZTz3yYX75d/4+IrmOqrs89PAFfvvLL1Byie//4Q8xXd/m7Pnv46sv/Z88evlZvvLV5/noJz7OCy/+LhcvnEbnPR6//BhHe0cMoy2yg5TRuYDSznh59wDVnaFkDy/MSQYFr117CeEnPP3Yp9C6ZDgO2d05ZDAcMuxuMZtM2d7sU5cF3U7C1rjD4ckJo7UBlx4+y3RSoS3UumFre43bb+/yPkNJv6vh9hIDjcEIszLLvHdglYBepXO4yFUJonHOYkKgdcP64BF8DG/s/A5nhz/AuXN/kofXz7t0JWOojUFKx5zJM72Sa93ve7I8aC+L6Kb9/6UMLwxDnJfZu82D4R6T12iD9DyUDGis8yq8P31o5QV5XzOzLkqMV3Fwdx9qjVEGUWj8wZDh2jqj8RZ6aFCe4HhyTJ3OafZdk2S0NmZtOELXDfP5nNBzLOuqqla/a/lnCUAs5Q5LVs5S0rX0RYTWf6j9LCgPUzuj46ATowtXzC5/fgl6LVkj1jrTac/zeOX2a4yCPjv5Ll9/+Zd45/gWF+LL/Ikf+mlu3v0KZTllnt5BN4WTaUmL0S34xbcxq/0jGtZaFAaMphd5nN4YcTiZc3e+IPYlVVNT1985G2N5TZayz/cqypaFXtM4L6NlA7DRGmFgkWfUdU3oefieS8XVmQOEwjAk6rizuxX3AEorBRgXM03LkFaBj6cEualJkohuEiGMRWknOZQiYFFUjkVFSVMbnr5yhcef+Rjd/ghdOdZ0HAX0z1/g9OnTrJ/e5Od+7p/yq7/6y7z0jS8SRRbfV8zmjikjBTSt8CKJAwJPUVcVnSAiTHoY41QMUeAT+D6zNEUC2+unMbpmb3eXGzduUDXuTH5x4xSXLl1ifX0d3/c5Pj7ma1/7Gk3TMBwOneSxcfO9yDNq3VA3DSZsG99a4/lOSlU2Dcr3sEaTFimIhkXRYHXl1h5jyVuf1P+/jNX1/y7AG8cacwxFhLMq0boijEJmM43ntdYZGMq8JohCiqbGCIji2K0dxuAJSaB8yrpmZ/cOB8eSRx58iEWaU7Y19XSeE0hHLgg8B9DUZU6tIa9q7h4ekhYlvV5vZaGSlY5UUNQNjTYYBMlwyHC8zub2Fo2BX//1z/G1F15ACoU1Hrqp6EXOR3SYdIn8wPlwWo1tLOlsytpoyMHOXZ7/Us1gdI7aeCRJQtPkdLtdyqYm9BQV1nnoea1M1y4BUgnWsExWu58ZJXGgqmyW7kL3pyu6r75VSiWl9/vWwv+31hHvq+r2lMeZU1skvQiF5WR/lzSdo+uC4XCEJ0O0rtqEpho/MKvuRFNkZNmCnZ0dojZGWXltR7N2m87SYHie5jRNQ7/fJwhCF4fcpj75vs/58+fZ2toCLHt7e8xmM2qjeeCBB+h0OuR57hgtQUDRTdje3ATA9z36QF1rdnZ22Ds4IooiunHijJMaSZrexVqBpxSqVOxWOyuDoqZqqOrSFdmN4eDggDzNEMKSxM4zYJmIUC0KLJUz8TP34imb1tS23x86x3jPYzjsA5Kun/DYYw8zmWywe3efuq4ZDAZ0Oh3mc2f2VFnJdP+Q6XTKdL5wB4XGHTh8L6A37rcGtinzquDUqS183z04SZLQ7SX4vtfGHboce6stcRgSJxFKSU5OphjrMdwaYYE0TcnnC44PnHlVYyAJYxrPOegrKdrO1Pu5mz7YsUxxWI6lYZVH6VS5jUR4sUNQhXt8pHIxyRmaMOoSJSPyRYrWOePxOYQQ1LpkMZ8TdDokkaWoCvZ2Djg6nqBUQLOmiaOEqqqoGnj0sYfxVcginWO0paom5FnjGE8yRAlQkcL3BXffucXOzg5X33yDKIo4d+4MZ7ZOo6RL3fEs+CokDDSdZLQ6zKX5gvX5CfNFxmx6wu3bt0B4bGxssHV6i263T9RJwGiMcLp7rRuyIgOvIStKkALpK4S1QEVdNDQGpOdAHM8TBKFHWVQOuQ88lsaICIPEUeAbW6NNQ5ZmlHlJUxUURUYSBYgooK5r6tp1MdfHGyjpUxc1n/21z7Gzs8f61iajtSHgDJC/F4FCJQMmRxUYqE3JeDwkjiLyxRFlNqTbGWOKGYsTwQ9+7E/xG//yF3j8obMc3tzFH0SEnQ5+Nyav5mxs9vm9V69z6uguKnDd2yw9YLQWcer0Om+89Rpe4FMVHkfphCqp2RgMmcwtUmWEcUCqJXffOWF74zwnHPLoAw9QWsMvff6X+dOf+Clu3KkJuxP2Dz22tiR+JyarpgwGAtMc0V/vEdWwKGb4oaKoc9JyjpWWyLOEgUdeZiTdPpWuKcojFscpTWUJlEZ6BQhDJxIUdUY6TzlKnafUmtQk/QHpouaF115H5ymXTiccHNwmrMeEnqRzFqQnKSXYIIRiQj8e0wkjwqjL1bff5mZ5wKPROVQjOVhM6CkPPw44TFO6cR9P+IggwQ8iDg8m9AcVTVVy5/ZtOmtDiAPmx7tEnXVuHx3iiZSt8yNsGHJSpDz68CZFuoupC7rRgH6vQ9CT2KrDO7spWWFpjubEkaIz6KEbOHvhHDqt6a1bDg+mTnYhUjz1wTIUPSk4OLhFYGqGKmTnxh7jK5fIsozttW0imbC7u8/DDz7B1uYaV998mduHtxmujZFSOX+69C6bpzbZ3hxxfPAyN+7MUH6MUIJkY05da5T0GSXrVJkgSWO6yXnK8oS17S1u7e1j05TgVI/dO0dcv37McC2gCEuKPCL0Y/bv3qEoalT3kKPpbQ4O11gbDunGCWW14Otff41PfOwT7O7usr15itlkgvQVcRIyKQJ60RrXrr9O1PWYFUdc3bnDgw88zFp/i8IqDqZ3uPH6K3zuF38VFYQ8fPkyZy5c5HznITZPB8zT8/zlv/R/MMvfYP/4FX72f/0XbG6sk03h05/8MTa3+nzx5z/Pv//jfwUQJKMOL766x4VLH+eNO68wS6/yxJNP8aWv3qE3PCabKAbre7zw6q+xufEcPa/h3NaI67eu8uTTT3J8eIRnUj7eqhnfAAAgAElEQVT64cd5/e3XycuaOBmwe+cGo8gjFz5jFHGu6I0DFouK9bVz3Ll9wqsvX6PTDbn9TkoUxSivw86t65RFRpJE1FVDiSUsM3r+CKoKaQsCm7M4Djg13mR2eEgn1GDqD+xetBasdh4q1oLRNVJaNC24oC0C36UrCYGlacGYAGNc1zMKt6iKA7q9K0hvzsee+Iv0o4hIeSuJisFJJQwSI5wsZWnea61FeBJPOGcOz7qUJWudAbIQsg18uMckv581sfxaa1ep1nXtgLGmXPlwLI1sPc9bNd3c53c+kvPZhOH6FovZHCUk/loXKSXzeUa/26MqSg6Oj+mPh6yvbVGVGYvZgqquiROJF4V0cAVrVVUufGF5tm7uMX+WAI6TnNQEQUiWloSR33qquU542dTOW8WCb0EFbi/2rJPw6FY6ZlsjYHEfCOZMTz2qoubx848jlc/85ow4WeNSeYrrJ1/l6NY/Z9S7RBINCIM+i6rACh+paxRgrGN6fpAed1o3pIsZ9WyO70VcufwQftLn9vQlZsczmqp0Sbrf4esvmRwrI9j72BPvlvBblPLeJbtDO3JaljlrhDNbWyAdI0eFIaHnY5cSOCWQnnReTAIkrmlscUwdKaX7HOaeXEtYy3g85uBoQm0NcRRQexG+hUfPniIZrHHxysdZO30Jg6CpDXWekcQha5cuMRoHfOZXfpW/94/+Nm++9Sq+L1A0NIVGGYWtPaxfEfqCIAiIw5BykREIiTWaThgSBAG+51GQ0TQ1/W6HQPmIxnDn9jvM53PyPOfCpYucOnOGTz7zUa5cuUKSJHzhC1/g4O4dNkdDPv3pT3Px4kXW19fZueNqkul8RqFriqbmzu4OWV2yd3TIzuE+s9kMcKBt5Htoq7l27S02xyNE1z2HcRCvjNM/uCHeBcL8q8CY9/L4/FcxN97re9/6+ivmyBJ4xrZSOEnTaI4ODvDb9+d7PkY4sKYTRpQI0ixDWheMsFxzNsdrAMzTGVffepNT66fp9PoYoynLilppKnPPSF5XJZVuUJ0EP4owUnIwOcYPI6IkZjGdUJYl6XyOFwuSTsJDV55iPF5nspjz0//V32R3f4+N9S2iMMRqQxwPicMY5QcrZlHTNPjKw9iaeTrn4Ycusb2+zuGdXUyVUxQFs8kUz1dIXxF4isOTY7d+93v4xmtZnBHGNu38OkcnrEEKi0VgbA2ttNexBt7t+3U/2HM/aGOw33Ew0PuriARErfzJ6JqmqVYLfxJFIBRaKOqypAhL/LJcbW7LSEVwHjZNVTsmRxhijSAvK4ypkFLR6/VYulWnszlpkROGoYsLU4rhYEyR5czTOXfv3sVay/rW5sotu65rsswxXkLfa4EHg67rNnFKMVssXGcjSqirgrKsieKYWmvSNCeMopW3jFIO2W2EA5LKsiTPCrLMgShxG0+otZMzeX5retfS+QRtclRWUJWGMPIJAieLcuZnZft75EraVZea2WzmFsQ4Jgxjqqpif+9wZb5cFhWL2YyicGilsM1q3nzfp8hqlsZ9/w9zb9ojWXaf+f3OOXe/sUcuVVn70ht7IZuUmpRESRQICbA18HgGMjCGAdvwC38EfwUbxhgGDBj2C2O8aDASYAwsjOgZUcPRiKJIkRTZJJvd7O5auqqyKvfM2OPu5xy/OJFR1ZzWjChLNbpAo7syozOjIm7ce87zf57fcy5iKOmvrbJNY0gCb63q68Z9zVU6r6ZJWq/dQ0IIKt1Q5RVplBCGIe2ktY54mU+wkP5tOj6WBV0dQp6DqISjjFvpLKFy5dxREjyF9HykcK0LifLRuqZqNFWVuemHEBRlxWQ65eDgCGsEXjsgywrm86V7D/2QTr+HbixHRycYA5NJhsS9X8L3EdItyHRdcTI64+j0hDgOabVSOq0WcRwicZWTdlVZ6fsRke9hhKTRmiBMiOOUWTqj3W4j9h4zX0wxAtJOG98P0ZUDg2LseuHnGhRitDUI3YCn8Fa+VWXFir2usTQgPKSQBL6/hqJJJZBWIVdNQ9JoqjqnbDRZtsRqixWOOi+lxBcKjVuEe9ItoIWSDHp97j/a5e5H9zk+PqbT6eAFnoPr/i08mlpDo9i+eJE8O6XMl6SpE3Rso6Gx1HnG1sZNrl66gdUwORuhRIs0TVnWFZ1Ox11PyxrlBTx88pjtjS5RENLyPK5dvswHd94nbKecnY1pvdGmlpIfPfoBnbBDucgZRn3m2ZT+xQGj8S799ibH0yNeurhDGKQ8enxKGkvKssbqivlC0G5r8qbC8w0bwzantkQ3HovlBGNd5K1uNI01+J7jNijlWgTMyv23XMyYz318FRF6ruoWLL4f4BlDUddMZlM8WVNpg+8P8EPBpZ4g7acMe4LTTCNCV8faWIPQjovhNnoGpcS6RWSymLnomq8wlUEGblpE6urp24mPFzrWxHA4pCpdbMfzPAbDPqP5HBFIiroitJbB5hZMj/GiNlGcIgIHzmuaAiFcI6FSirxaYqoFWVHQ6mzhx4qDR/vsXL+JVD4ay+HhIcQRWZHTbnXxo/C5n4+er6iqnDSJaIqcfLIgUD6LWrOYLynDmo3eJrYJefLolPmsgjQkaXU5Pt3F9wTSlxwdPqKue3gUHJ3u0e9eIIy6HE8e02n3mRzN6AVdPv3iZ/jonft00hanyxPaaYCvakJPMVtMWZQlgdcmUoqldPeSXrtHU2narT7a5Owf7XG9e4M48hl2Ntm4sM2TJ0/4wQ+/z6ffeJODgwNarRZhFFPrmsl0ih8oZvMJZ+MRpVxy5dplvvmtr/OFt34N40dUVcH9u+9zlp8ga5/0rIeJPUQU0SpB2wV3P/oO9+4c0mlv83f/45uuMUW32fvomP3DKdaGHJ8d84U3Pse/+M73uXrxC7z34ymmnaCkz/HJE67euMRsdoBSW0wXR2ztxBw8rBkkliTyKMucrCgoyooWASenByyyU3qbV5lPFni+QCoXF9re3ORKcp1/9JU/4a2f/zwHBydcvHCdex+csrE54HR0SrvVYzqdc//eR+zs7LC9vc3ukz0uXNhi/vgOi3KKTBRBWqOspd/uMZ/MMaah04sQz1dTxKIRqxpuF3EAISxGP+2QcdgFsYpjPAXsNlbiU3IteZlCz0jTl4mFRFizimmtVr4rZ4du6vVm7GMMCeuEYWMMZeV4NmK1kW6aZs0DLMty7TR9Nlpzvtmq69qtWRtN4PkueqzUmtNxfh89B2mWZUngBaRxi0o3tDtDpATlRWhdkySOV1ILi6kLpscHVIs5F2/dAinRq4rxZxf/YRiuHeNuXf0UcPxx55Hj3Hi+W++5NtGCKHI17FJLV18tnzaw1nVNq9Vat8Io+bQd6dnXs65rgiBgls8RVcXNy5+i1bqErY8p73yD4fAV7n7w/6C2XqKoM7rti8xmuw7QKxVKSWzzfOMrqwoL6qpChQGdtMXtmy167zzkyXjuBpLCbWb/yr9j5ayBjzdOPXucoybOzxljzLkhjVo7McjzPIrKgWBDz8P3nracnf8elBNwWMc77Fpsc2BuQxB4mMZH1zU8cw4FQUC/06HRsHXtGhsXr7N94wbGb5PlJcJogjghbaX4YYA28M0/+xa7uw+pqgKp/NUAztBUntvAGghDjyAIUEJSlg39bhu52rQnkUtcLEfzVUw2WbcMTUdjNI5zcvv2bV565RV+7Zd/laqqePjwIR/dvcd0NObTr73OF37+LTY3N/E8j83+BZdmKAoIPbQxjJdzFnnGhw/u8/13fsje3h4n4ykYi/QUcRy736kk4bn4isI8yzZ5DsfPKl/+ZeI2H4+CfvLxbPuSxYJV68iltRLPs+TLjGxZ0GnFK+ZpuXaZRUns3E15QbNyGSucMO77PpvDIXEc8/BhznQ+BymJohhjDFWuaTxD4PtubReEUOp1E7AIPLDauaqWCxbZ0rkK45CLly7S6XfYvrCD8BR/8E//KYtsSbvVdYydxgkrcRATBhGB8pDOnrbmVGmtyfMlkfRopTGT82uqH6zdmMvlEpmkdJIO+wdPMNbS6naI/IDzRkzB6loozOp+s7pGOrz+6pWWf6Ej7/x9+Gmx92Pf/0teh34mEcdoVzmqtXCgUy8giEJaUUgYhmhrafKC8dhFOuSOodffQClFFnkksk3UTjErdkdta6qqIi8KJpPZOqoRr/g0WVFxNjrFGMOFnUtsbm+itWE2n/D222+TL5fcfuEm29sXiJMW88kY6btpiCdc1VwQBBweHrLMcy5ubuB3fKbTOUkQcnF7k0F/wMnJCbPZhOVyifA96qJcq+BN02C0RnkeeZ4zHo3ACqbzGU+ePCErSnqdLlESMzo7wtgCJSTtbhtEQzYvQTREUbQm1YelY/UIC5XQPHjwiLounUilBGVR4XkBrVbi4MW15sb16/jKI1vkLBcLBoMBw8HGKqKzx5MnTyiagqouiKKIMAxpJQM3iUIjpXNbjKcj/MDdZHwlEauq6el06pRyL2Brs40QYs3eWSyWTEZjkiQijgKKsiZNEzrtFv1BB2sFZ49OHLjub9HxbyW4r75nVLB6zOqDJgy+ACNx9HovImr5zLRHnpcONKclpyd7LIsljbAYAePFjPl44RZXUYAnPMpKczzapTfsscgz2p6iaZxQef7eSqHxPB/QlNWcLCtAN4xncw72HlHVFa+++irtdpsoCPC9iMgPCIdbNJVzvNlGM+j1V80cNWEcMcunhLMzLgjJ5vYWR0dHlKt6e4zjU8Vx6hZ7CsBQ1AWJbRNFEUWRQaPB88BawrRFIiRSQVO7TXEQhURB5C41wkG/fCUIfIn1JGVRsFzFw8piiRAO7J2mqdPGPCdGGdtQZLkTCi10ewP64yknB/v883/+FT772c/yxV/5ZSIVrmtKzw/r5k8ff2+f80SlsoYf3v+Q33r5LU6eTIhEwP29P6Lbb3N0+iGyNWWUzdnoxnzjn/02L7zyMpE95vH3jmlUw539e+TBgkF7i9kop9UX/Ks/GbO1teQ3f/0SnrgGtkfAGL/x2R5eoJGFW9DlLcraUns51UjjRYrpeMRGuEU5XUDvmEdHC7YvBry6k9C73iHdu4sfXmO5PGB6GvLm6z7qwmvsnhmu3ob3P/w6Iusx2NKISYURI3rRAF3G1OGYXFtaImKe7bGsc7JyQpTewCPAhmMK3cfzltQiI45bzMqauipcE+NCUm7mqMGAz/3ybfzGcvTwAB+B8S3zQNPLHPcpV5K8cg7E0IZU1mOaZ3xTjXjzs5fQ4zG6PSRoGmx3m57UfLo/ZLeokJtbNLJGExLHCU2RUVQlYehztjemc/kqokqZTKdYUfHCjS3OZmOkLBCLENnr0tosiWjTTpdIISAI0NkEkaVs3A4RtSK41qIuDVZCGMW0Lm4zenjA9jAh7Uja/Qjx7nM9HamqAhVGWAmzas6n3nyVMEhJOjVZ4ZG0OkxGH7Iwfap8wtWtVzFezcneAadnOZd2rmKsR2xjZz+uKrrtAe/fvc9LL70ItWJ6NidN2hycHCMI6F/q8eg452C5YLT/A4waMFqcIfcXPJ6csnXtNqkJ2Z5tcpRPmOVnDLcvMjvZxYZ9ZJqx0BmdWtPvDzkZnfHZz3yZ0/E7/P5Xf5vf/PX/jKOzCWnnIifHd+le2Ob/+t3/nk9/5hX2sl1uDy5RF4Zc3OMnpxqZBUSyS86Y1lZMtpgxyQ4JFyEnux+hD0ISv8Vbn/8NdjZv0+2H/Omf/ym3r79BNq25ffsyeX6J/+Y/+S/5P/78d/Bsj6vd6/y/X/9tfv6tT3N6+CKmPuLGrR7H84+4duMmd+4cceNlxWic0+2XLKxgNptx/coFrg6GVNMl08UhIo55vF+w1BlpxyebeCRhxevXP0eTzfndP/ltet0h7797wLVr1zibPCBt1ZyePEAFHnmZkaZdbrzyIk1jeP+ea8e8d/8OKI0fNwg0oRowr8cIs0D6mlCCbGo88TwDqdY1Mkrj2gVNiLEu4iV4yhHAKkBgbIi1DVrXXGxfYie+xO7iAQ/KMUE04Esv/RckbQ/bKMpV9PtcsHCii0RrJw6c3/fPN7/nvBgVBY4jshJpgsBhAM7Xn8/W6J6Dfeu6xvfDVSTHsqwLx9EB/DBEwsoVpJErR4wbSiiWyyX4UGU5pqnR2pLPJ4RRC2sapJ+ghGCwtc18MqVpDAePHrO5vcUSs35ueZ6v+I4RaZqiVwO2KIpWm/un8NyyrinzDM/zSJMOxp43u2nyYk6SJJRlSaVrpIoQKLchqTRVkZEkCbO6xliBr1Y17Agwbr2jlGJRLJmVCxpbsvvOt7Bpl0l+RqwMYXyRK1d/yQ2cOOVs/IDt4SvoJmM+38MisM/ZoSitpjo9QxSafDmlIzwGwzZffuMWZ+NTDieaaWOwalU1j2vVtWaFbhIg7NPY2k8ziZ46o5r199b8pWcEQWvtWqg5j7oFVUhjKyppuT8+4uatSwxbIS3jhhpWWerUrf3iMKFGYa3Eeh61KTG+c7FZitXP71CLHCkt1ssxpsEPPLYv9tCNQEif+fbrDAYDrr/wKmHaA69NVjVEoYcqM7Y2hwRKcfdH3+Z73/seX/nd36HKDFZHTKY1TWOwFpRsUEDcSuh2u1i5YjhttF1cX8PGdp9hp8fkbETqBcSpR1YWjGZTjNZsDQeEns/rL7zCf/73/gHbW1t8//4HfO0P/xWzyYSbV2/yuVc/w5uf+SzbF3ccM7UsSFPHNvWiAL+V4Pk+InBD0aZp+P53/5y3336bf/W1r3JwcEBVFUxOcy5d2GJZ1hycZCitGbZL0M/ZiSM+GVD801EbIQTCFe09fcxf4Nqx1q6a5H76G9INX90PXP9cB3SHj3GUQp/J8SF+5K9b1saTGTYv3Fq+LBFK4gU+oYCyLNePU6vrnRKSq5cvc3x0wt7ePlprgjAmThO0dWKKweJ77v8ryorsfLCMEzKNMRSVE4ujOKbV6xG2WvzeV77CRw8f8ejxHmGU4EsfLwhIVIRE0QpbxFGIlgaxMiUY7eKo4/GY8XjMtUuX6Xd7HFiDsJK01UVry3JROCxFXuMHina3jzGG06NjfD9kc3NrHYN8Cnv3MdqAOG84VCiJSxl9QnRZnjcynr//2E8Ujv9G4lTGWPKmQigPJRXSC4kjSavToqwarDYUWYkVNU1dIsw2UeQYG6npkTSGuq4YZRXK94iDLmdnJyzyDDC0Wo6rspjPaeYNSZqysTGgLisklqODQ5azBdPFnG63y86FC3RbCfP5nJPDI/Q5mEkYLmxtc+3GdZqmYXd3lyAIuHzhItl0yejohCtXrrissjHMAg9tDbuPHxIqD6TAD0OEOmJ8NmHQHWCFZDQaYXCckqf1l5bx+Iw8z+m0fG7aqw6o7Flmizl1WaGUogwLBwrVGms1YdgnDJ2a3ev1mIxdbfTdu3cZTWbsXL3MrRs3Xe1g3Gbv8b6Lms0WZMuSs9MRWlva3Q6feu1VtrcvMl/OePjRA+b5AmMtKhX41qOpDZUoUR7EcYTvq5VrCayShH7Isqrw44hABQ4ohsBUmrPjM+qqIkkj/CAgDALy3NnPJtNjmo8KgshHePa5AzyfVTk/yWXzSfbBp38wK9dNsPqCWf0DnjQOFI0kkApjBGkYobsDsnLBaHyCnwQMky7LKufhoyOyLENGCiEUZJbjE/e1l1/+FNoa9o4P6dcltW5WooMkCCWXLl+gKjV5XjKdjEB7NE3FYjamvzUg9CM6nc6KATVECjeRiaIIqzV5VjIZn9HIGj8IiZWLJIY2oC3bYCVRKyVptdwFOF+ihaE0FR4+vpXgWzzPR1Ql8/mUXm+Atc7RJhEEfoQUcr3okGJVrSokvpQIT+BJSa01RtdUlcGWNeWyoFzWNLpGegoPs4IhhwghsVW9yo9D4MekiSEvHA/r1u1rbM76fPtb3+L9996j2+3ymc++iWm0a0mSEnVucV+Nls2qIvJ5VzrrxtLdkJwspniDgF78Fs1kjGXB1YtfQjeSSA6YBve5dfszHI+O+eBxxu3PX+XRwR5FM6fWAvoKHRbMhOHLX3wNz9YEXoMKKpbNnMH2gK3tPvcf9WlMh8VsxOsvf5r90bu0OzETM0YNzhj0G5LlBUbjKR8dBWy+dcrZbM5v/uYv82d/9G1+7T/4LF/7+g9QQZfhdkroFcjONrFMODp8Ao0mWx5BUZCZNr4IaKUFhT0FcYmqMsyXcxbLEWVZYm0b6QkMNcrT+F7g2GNFjQ0Ffl+wFW5jlzVe2qb2QlQck3gGX4M83OPS9hBpGkSSclIs0ELS6AptDJeaiJkH14OErx6/y9W4w+DMoNpDfvJkl06SciG4jNANUbuNJxvGyzmDTsh4MSFpxSwqB4ofHzgnY1MtuHBtg8OzExLr8e6jj7h+qYNebjGbzti62mfjkuTkwSFhdNVdS1REEAmyape69LB5Q60VJ4dLaBRXLw/4pc/sMHllyOHeIYvZnMmK7/Y8D2MsnmwTxAF5syDpBbSSCxgUd2fvIULLTEy41rrFOK+5c/ddLl65ThxugGkoC7h24zKTuWu029/7ACUjbt2+yjKbc3PnFnXd8O5PPsBaxfUbL/LB4QFSwqy22Pkpg46kaV+hmVa89tobvHf8LnLYJz0ZcKF7hdHslLBnGLdCLvZ2CGLFnd336LZ7jKdzptMZycWYfG547cWf4+GDexzrY1rbMT/88Nt8+pVf4u/8xt/na9/9HdINwZPTuxSl4uLOFb79o9/n87f/a4Qc4PfeYziJeOGVV1mmhlKdca39WQbdHXSh+NOv/4if//xrfOdH7zO1lrfvvsvtnZcZjUryPOPeNKBVvMc/+97/zH/1ud/klZ97h69+7f/k1s2XqTBMF5p8Ydh7sEtd19hmi9loRDfxyfMG34up6iWL5Rkf3n+XX/3F/5B33vkut2/fYjRbsJw1xEHKtesXePDgzzk8eMJwq4sf9lhmE5blQ3dvkQrPU8wXC7RRzKYlefYY3w8YDK+wLCzzrKIdhywWpbN+i4Zq0dBq4ZyMdc1CW8rm+U2cLRZhHTfNGIM2NazaDKX0kELheQGeivBkgFKCuljSSSP2iwVH+fsM2zd4bevv0O/f4nJnh+ksW7tnEuGchefu4lo/5ZGcx5vAoHxvDdZttGutisPIPV6sNk7SQ+tmff84/zmuoKPE2mI1rNSEsauhb5qGxSJz7iLrnM9hGLJcAUAXs1OElARhSqfbp6qcs6auDEZKiqJGzI7JiiXtwTZbOzuUdYWuXWwqDhwz7/zvUpbl+v4PIISkrpu1MGCtJfAjGlNQ6QaBYZnNSJJ09Ro54SxbFqSpa8zMlgXdbpdaa5QnqCvNvFmSJglFUa25OEIIpLGIlcDlactWd4umabj41j/gwd77/NmP/9HKkVSz2X+FJ4ffpiobuu0dFosn1FXBoPcio8VDbOOiB8/tXDSwzCtsBdJapPWZnE1Io5CXb1ylO5ry8PiELDNk5aqqGgeKl965mPOM8MjTWIvv+2vhoK7r9ZQdzivcHeD/qevZIIQmCAI8z6MqVxE40TAejymqkq1Ol9hY5tkSKXEb3iRFeYELokmFEVBbg1rF2I3E8Ypt6XiPEtK0jUlbNBqaWtE0ls5gi53XPkeSpIRpH4tCeB620QhraLdThIB3fvw2/8P/9D/y/vvvc/9wlyAJKEyFblaNkZ4iDSPiMEIajakKt6arKkJPIi20w5BBq4VnDOOTI2zgPptlltOUJdvb22wNt+i3O7z55pv86N0fs7f7mDunh1y5sMObb7zJF976BVppShy1qLRhNptRNJq4HeP7Pr3hkLTXASFYlE7s1FXN1tYWv/iFX+Dn3/g03/7un7G7+5D79++yXCyIAo9aWCqteXJ8Rm2eb5LAWqiFxVsJAt75INnCuUmx8Rz82zMWVq1+mpVrRkhXqMB5PEeDEUhdYoXA9yOX0dOSqtEIEWNpEEiSyGFKlBL4XrA+VwNfuabZQtOJEoTWZHVDlESIVRTz3A2mlMIPAqySMJ+vxZwoirFGEkctLl+Oqeua6cJ9/3DfOWrjNMHzFIEfQQNp2F4lXowrMZCBE4yLiuVCc1LucffuI5a2wFYGz5f0ozYSQRJExGFMmCTu2ugHpGGCh6X0HZNNhj7LLGN3d5dOr89m2gIMJCFFmRFLyzSbYexqWGcsVSnwjcb3Q+IwwdQN87wganfAWoxx8dSyKFHKp64sYbhyGNVO0BfCDS/W77luXOvi2q2zSup4nyTM/Q04cYQ4n2o0qw2TXAHOBLZxL5aQDooqUNTarLNy6YoL0ljtWlmUh/UcmNXzXCNPHMfu8Z5H8wycTSKwQjCZTNzirpXS7XZppyn7T3aZzGfuhlTW1E1OHMe8+OKLdDod55ZZVWI3TcOyLKmss7yeP7emaWhqgxKConK16FHZYJVkNlu4hijjJiJJK0VYSLwAtbGBrmuWuWskWiwWnJ6eOjdRHK8I7W7hkGUZeVmsXr+Ibte5P6wVtFM3kVkslyvVMaC9atyy1tnW0IbZMuPo6Ih+f4jv+/zkJz9hNJqAsWxuDun2O8ynM0fXNs5d0263gKd53eHmgDB8Coz2PGd/1NaS5yW1NAhjERYmizmjszO01nRabeIkcbGhVTuYMS77GFQRQRD8/wJs/XUfP21j+zf++7w2VJznUc+/K5/aDHn6/infW03kzplGLaqqYDwbu2jQ+gbeoCt3kw7DmCtXrvDo4ROE8knTitFoBMbS6XQIw9gJGmjnqLFibTu31tJut4jDmCgO8NSKI+W5iaXyBHb1b9930x6Xi9ZIqZxASOCEKmPWMT0l3LzG933SKEZjnTU8TJzdcbUwFUKghAN4+b7vJK6VXTxQAXbVrISUWItr7BBiZed1i+QGvc59WiMQvuNgOVZBQ1E62KHvewigKD9u6W4a13Q1HU+4c+cOr776KkK5PL1y/vuVdfxpXawV4D3nCvLzU+vB48N7v00AACAASURBVMdcv/o6cfAKV5M2pb3Lgw/OuLB9mbv3fo/PXoqJ2n382QFHuz0GW4K0m7BZ9ykKB8OuTcXJ6SG9dorvS7LlAtXESNpEYR9tGyK/w2JhKAroqwSQCOWT2xyv3RB3PEaPM+bzCpOl1M2COPEJU8V4cki75XF2tset25ewqsKqCi3aCK8imy6Jkhaj44e0Ah/h1Ugb4QcaP3Rg9bqpyMsGY2uUZygrS9VU7lxWYJqSpsqorYt5hm2fKp8jfUEYxTQIirpkNhuR4uGHHmESQtMgfEmmKxqEg3ALRWAFwkpiP0DXNbfTHqpacjZdIIRCakuQhtTzkqKqEJ6PXgl6DhouwHf287IuyLWmsTUlFVZYB+j3JO1ui8cnc6rGki1rwKI8i/IjlGfobQ94cvcJcaLYvrDF7GTO2ZNT0jgE61rfglDSjkMmZz5lrtCST56M/Q0eUkpu3niJ7//w+3RbbR493iV54SpoiIOQJIrRosNykXN0eMr1Kxeo65JGuzZCYBWTK2m1WvR6fUajU7rtLcDy0UePuHnzJmGYrqLEEfgWg0SqiPHoCNEEpP0lKoByNiNqApazkqhrCTcGKEpqOUNFLYp8ybg5petFXLy6wXQy4Ww0c+UIYZ9Huw/5hS+8yT/+X/8XvvWdP6YuZ1SlZmtH0diC/bMDNjodTkcnXLjmkxU5d3b/NZeGL3Ox30Fqn3wiaG0NOR6NiLtbfPDhPW5efY3rN28xWpyyfXmH93/wh8ReQmGuYMoOwmuzN/qQTvgScfcRX/v271NuXOfm7YvMZjPHElA+WZZR5D4vv3KL05N94nDg7qXGsMwrurVmNHWCWFnDbFYx7GuqPEN6CYG35Oj4CcbW3Lr1On/6p9/h8tWGdidACcF8snSbl0o7Tp4fkecVVem4F8usoKglYRC74Zj0MaahLtyiOs9LWqtYedLqI55znurcifDUxi+pyobAazDSIlSIrWsaWVFnJVvJBmNjCGWCEA3d9AaNEhyNH/Op3mWO8xnbrQHGaHLhBoNK+tSNEzE86eJN/nlE15OY2qCxIMX6/mYFVI1B4u5XUoFU/vo+dx4xsivmjEKs41ZlXREEEcZkNEY7t7ZwXMDZbIbvK4q6otGWMIpRgXPxJImbREedPnWeMR9PGFclcbuHlJIiywmS6Bmo8tOq72oVAzsHDjdNs2rfeer08DyPqi4IfZ/ID13LqWgImgYpvLXYJSVrRo4rI1lFeMqMMIjXvBYpnaN9zdmxltoahDZIIXjvyfvM82MGXode/5JbRyuFEA17x99j0L3GeLrPIjuiHfaJk5Cz+X06nQuMTnef69jPvZxurdEYS15Vru1TSDZ6XZCKSjfsNxplXTuOFS4urXGvcaNX60rpij3O41Pnjhq7YkOerwmeBWM/C9c+P84FQyntSs9SlI1r7XHikIcnIfAkgZJufcUqviFcS5rC1UFZ62rbBQZPGKwwDvy9isz5UUJVK3zr0d68Rne4vSqhCEAGiNXjAHq9Nm+//W2++Sd/wrsfvsfJ6JQw9VkW2XmZKWHkrduFfSXwpXL7jNU6LBAuJtlrtZDGUFY5voBlWTknt6/wZEIcRoSe2weenZ3x6N5DZrMZnZ1NvvjFL7KzvUMQBORFSRS7aLMXBsSruvEoiWm322jAWLNmQ+WrWvZup8PGpcucHh3S7/a4dOki3/n2N5nNJ+jGFdp49tk9wPM5rLArDo378yclByTClflpg5HOtSGMXYNwzx+rEVir8ITjTGnbYGqDFBKBhyfO29Ai95qFbq9tpUD5PsrgzAtYpJFIjWudthqx4pXp80/rKpbaNE6obrVaa2HHxe0VXuBjFk6oDxOPi203RC5WQo+0UOYFyvoo3J6hWbkpPRlSVQXT6ZzFbE5t3HPIbYlRhjSO3d8Bx9SNle/0gyDA832k75RMpRVKWhqjaaymms+pdUM2m3F6NnYFR6ErSlLCgnaOal2XqMB30HflnpNEkIQRpTHMs+XapRnHPkudIyy0U5e+qUuBtY5rKiwrhw5oNNJYZ+/7azx+RhFHIKiZTE/xpSJOAqR1NxPPl5RNQ5FXVE2OFwQ83nsCSLrdLknaJowdwE5tbGFxTU/WWrrdLtl8wWKxwK4uckEQMB5P2draYtgfcHQ2YjQaYS1sDIZYa3n8+DFVXTLsDYlSl4HXpsvlyzvcvHGboszY398njmOGwyFFVXI2OqPf6jCZTWmqmkB5dFttvMsevic5G49oqprGOJL1i7durngVDUZI5vM58/mcyA/wQp80aa2tt8qDg4MDsizjyeN9LlzawRhDELjnVhQF3d6Aul5SZI/YurBNK0kpioo0aROFCZ3PtZnO5+ydnIDwuX3rRT54/332Dg7dhigMefjwIWEY8vLLL3NycsZ3v/s9Wp2UzeEGL7/wIpPxmMlkglxNCIS0SOFRVjnL5RJrI6qqIkliut0O1koiIahz1yw2G8+Yz6ecnpysbMT+x/J754wIYxrCKCEMAqRS+H7w7zyH/iaPT8of/rv+bNdijkS728BqNvRx4JjvhSRJBBh8X1LVxVoYa7e6eN6SydkEtKEx8OKLL3Ll8jUWi4UT01pdDvf2GZ+d0W63ee2114kDyXw+ZTjcdtOxLKMpcmrZ4IWS2PcJgwQvkFhdUVZzPD91k0tPIJTAWIkfCbSt8a3ESHceRjKi4Wk+Pk6ciDnLIhfdi+K1aNo0zfriu1gsVpX3LsOqV5PFWmuE1Rhr8cKnee6nZHZ3EdfG1TdqrdGmcSKvBG0blHTwr6qqqOuKpqiIO21nQ25cNawQwkG5ZxPqpuHG1WscHh7ytT/8A15/9RVeeuklwjBYL8TdPxa5amT4i+o9/yaPXnfIS9f/Ux5N/zGd3q8RppvopWT3wX22dl5guZxx9YU2Ub3NV7/zT7ix8QY3d7pMH4bYCx9y/eIWzfIWj2d70JzxxUu/Qi4d2L2VHuDpGbrxsGKT/cMTXnnpV7h7dIdOmnD0+JTB1iX80GeWzFjmFdnREhaX0cZyc3iNOHiItANKKn79P/o82XLM7Zd7FOUTGtPjxqWbmGibd+5/SGMsRlm6VyLqZo5kTuQJPNFi2FGcnR6RFxW1EkhlCXzFslrSCiq8MMZIxXx0D082mFpgs5o0jdHJCdIP6Q96zNSUUhcs6iXLrCINfIQM6HU30CZnWpZoFN24gy8Up0LzEhHTRNGtLNe3+pzVHe49OOSt7S1skTMTFZ6Eqi7pbG1i5jle3ZC0ffLlnNxUGFEj2yHjB/tEGxE29fBiRctr8V61z+aFq3S7JXcfzcknFXHss3N1m2lR0d0IKMdndALBJLG89Man+PCdXeY/3ufWCz063ZisLDjLLMvmjHa7QyfuMa8XaLP/XM9HJQXf+MY32di+iheGTCcHPNy/Q6IkFzvbxEQcHFW0rsJgMODq9T5v//Aj0vQC125soZTiG9/4Y26/8Bmm44qdncvsPr7P4ycfMehvczJdkt99QLvdoSpqtnoD9kcfUeVg65A4vMTJyZJrGzHeC5v86Aff5/bN68zzCcOrASOtydIInzE3u7cp64KT8RNsAD95+DbepMfOlU2++YOvsD24hQ0sv/e1/51b129z9dYN/u/f/99Qhwk67VPonKTv8+M73+XV7V/l3sNduv0Be4ePEKbHW2/+PX79S1/gvXvf487jt+nFr+G1timDO/zk8T0GF7Y53f0xt1/4HI93j3j5pVs82vuIXu8a0+wxh0ffAW+TMJvww/wun552SJKGqkmQpCzzktdffYHjg4TdJz/k1q0X+PD9h7z+2mscnY0Iw5oP7z5EqgN6wx2++73v8Ok3Pk2W3SMKA8rCQxvNbDLC8yQ/eOcdur0LZPMJabhJWXlIE1GWbrjkKqYFgZ+Ad42iqWmLmDgU1EXNfFkw6PXxlWA2GlHmC1qDCyzLjAZXUvA8YbLWPq29dvdn5YCr0qfRBmENsi5ppAADSgj2lwcIWxOFXdd6qlLOpnu8dfVX+MHj97g8uMLeyQHdtMM0O2S6mHC1t0MctWn0knaauNKIOKLRDaKyCOUjVxum0A/cvWblqqhKdx9fLBZrvuE5K0drjfAk0ig8z8euCja01lS6dO6Kwokti+UpdWW5cOGiG5wohU1ajkPoS1eJjKtYruoSaSWttEPa7WGta03BkzSVYzc6fqFwLlnhYKNhGOH7AdPpFGPMejjp3K1i3S56vl4rshyvCigqgbcdUxUFSq0YJrZBNxY/UBRl5taKq/jP+SY4CAIq3SCNg56qFaNM+gGxF9JJe8wXu3z9nX/CVv8ltoa3mS33aVaDvtPxPvGKdVbaHFEDSMbjPeKow1+Z5vlXOJpGMxpnmNo1zSyyhryqOBvNsXlGhKYf+vjbOyAEGkFZVWhrGc9nVFXFLMsdkHk1oD0XYc7XUD8dm7LG4ilJGLiGHGuc+KUkBL5PFIaOPyQtlalRSlCUcDqacGVzC68TE5UhURySxoGLyQqBwKIENFoTqhXjEkAbjHVuchF41I3E+iFG+RD1SZIhftymv3MT4g4ycFE6rQ15UZCmEX6g+MEPv8c//If/He/95MfsHu87ccdY5OqvlySKYauNL4WrcrYaUTV4jeNwJcpDYul3uqSeYn56TFEUxL7EGCdCdhM3YA6QjEcjqiwnED43bt1mc3OTn/vVX+Da5WtYbaiLmqbRGA3j+QyUR2fQJW513bpPuQH8efQRII0Thl0njg6iFq2oRftSh9/6rb/Pl7/8Zd597x2+9rV/yejshKos+PdB9FRW8Cy31CWbHDz8WQeHEU9JN1ZJrDWrz7hbUytwDzAapI+HT9OAMRVKAkg85buEidBoNFGarFkxaEPVaGoswYoFWpcaf5XWKBelq91e8cqsdUPUqqqpa8dJDIKQ5TKjqhuMcYxXIVx0SAhBFEVcvX6Nuqzc9bOpaaqa6Wy6ath14kgUxijlg2hod1trFmIjGhqs+wxZCKWHkpLQD0jbLVqtFlIp8qqkyStX9e35YA26aqgKFwM7PDxiuZgxHk/pJ52VKOuRtDvURpNX5Zorq5SPj3TDcjxKKTg+PmY5m9PUNcPhkNu3bxJ6PvP5EoHB90IXkLIWK8GYcy6bwHoewroUCMKJrs+KcX+V42cWcZTyCFQAGHRTI5UgCgOUhSpzeUyrG6SNyaZLzuIJSvkEYYy3ct0QBDSNpChnbnLiBVjpHB7n8KQsy8grF+eYLaYc7u+TpBHdTh/PCzg+PqSuXdVcmqaUZUmWFQRhSG84YJnNnZAhJdvb2/T7fcbjMXmeM+j1WY6mqxPM/YwgdBDlLCswfkMQuLaCzWEfGcUIDXnt6sID1Xc3+JXDJ8sy6rKklfRQXfdmZFnG0f4Bq/dvZYFNVlZLD2s08+nCTbbrmlpLmkrT63WIWm3OJlNOj08wjabT6bD76PFqMeTesqoqyHOfJElot9tMJ2Oa3IFUr1+7xtaW46Ds7z+hqjRBYEmCYDXRKdd1mItFvuabSOlRVc0695hlLhsdhuEa9nfevJAkzrkhpAPz6apauUT+/R3/tmjVJ/352cNai7SeaxOTEmGegU1JQWMbZ6uLLTU1Ve5qSaPQwbB1XRP4IUZbd7G6fIWt7W0++ugjhACpYDSasFgsCENnZURLlO/TNBVVXRIEHhafQFpaRtOUNdgCQYhBU1QZfhggVQiicSr+it2EsQi5qla1Eqm89VTFtRU4l0xkLL4U6/aM80NrjfQ9rHxaRbp2qRkIPI/GSIRZwf/sUzCXc5e5u/v5YkbrGmM1UgqU8FwzyOrGo1dTPi8MUKvnaaUTX6w29HodZtMpWeYWlt1Bn8PDfe7e+YCt/pCNKztY3aCNRq4qDsHZ4SWsb+LP6/BUwPbwDT44WVCVAZPsPsyGBH6bdvsaobJc2n6D6dldJsUDguiX+cmHX+Fi7+8i+jnWdCjmNcWipsxruu0NMsYUtcf00RG3L3fAaIq8Yjw/4/r2LZaLkk5rwGR8SraskE1N2m4x331C208RNqTIR2xutBn2BxwcFgw2W2xe7JKNxkSx4s79uyThi4xHDYsngoO9febG0O638Fo1gbUgGgIPQl8RhgkjMwNboYnRRoNxTRie5yGUpCo1WmfEfoDfSDACpXziyKNpDHm+pPRLbOwRxyFFVq0dib4fYqpi7YRTSiEbSxZJpJHMfetA3AJGizn9wZBqtiQQllmxZOgFeFIQpCl5ZZClxheSAtDWUJQFpWmYz2eEPQ+hPKSENA7p9XocHByTpIKyLllOGqRVtFsReQnS99DLHN9T/Mavf5l/8dU/5OjRglvXbtPtWWRQMz2bMl0o4k5FWINnfMI4fO6Nao1uGPQvIUWAEAptFUfHT7h9+RoKiWgEVy5eB99N0RbLKUJahsMhWTZ3n7lemywrePml17j/8I8IQ5/B9iXKwpB22pwcn3D59cscLg/AWOpiSV0JlrMlrY5idLakKhXppT7p5hbzfEpez1lMLcIrqJsGUdTcuLjDR9OHLPMZ5fKQD+78hJ+79iU+/OhH3Hv8Y+pG0u/2mSz3uXzlTb72L7/OzVsv8OTeCUaM0FqzLOZk+ZzT8SlWTVmYEV/+xf+W1259CaMf8MH+PiaGo9ERaZxw7+AR957c58LgRY5GR3znu99ktgRTprz52hf4wz/4I3au3mBvesT+7iOm3i4vJC9RqAOKxSMOZiPa3VdYFpoLW1s82fs2yr5Aq5VwdjpiY2OLR48e0N+8yNnJAQJFK22zXFRYW/POe++ShGe029fwvYR8kWN0xcnslMFwg8ePTtkcxJweL9nevkgQRNTGRWo833eMFJXQG15mMRs58GLko8tVM2iVg+e5z0LgYYVHkReEoaLMy+co4bCynTsYpDFPmQcARkjkagAmkasyAUGgYgJ/A60rknCb/ZM/5/rOlwj8LneO/4B2vMVxvse8OaXjd9lq98hFxiKbM4wHa/GhbGqkcNNspQRN44oAzt23Ujq47lNX8schk89u0s+dL77vnFfnDgytNXEcMp/PmU2PGfQvUjUNgXfeAKrBA1sKROCvNlsKX7lN23QxJ2hWtbtxhK0rVBQipSQIPaqq+ti6Joqip+KSEOumqvMhzDkjJ89zhPKpdU05nRAONzl7/JjAF9SExJGLoZR17VQlWA/sGl2tN3eN2wmiPA9jn64NtK4xRvDh/W/xcP+7LIop0eKAVrdPXp4Sda5yNPoxkoDmGYCx27AIpAzIy5KfHe/6Vz8arZlNcpTyMQi0UBS1pSxrty7XhkAKWnGC8BRF6QQ7qx2I9fw1F8aueRbn70u9AlD/m66Kp0DoZ4dLT51Ocr2BPy92ACirZuX6US5KJSWeAKE8kGotQArpLCSOrSHRVrk17LnbzvOxXgQyRMVdov42YatH1B1ivADPD6iqmqpxwO4w8pES3n77e7z/wXuMRmcEq+fUNDW91DnuA6GIEXiOrkwUhETtcL0W86UAq+mkLUyjkUaD7+GpEF3mq02+B0jnDk1S2u0u16/f5Bd+8Rfpdrtsbm47YdGTBCpctVDlLIuSKJG0Oz382LnJy+opB+v8fhsoD5u2XJqjsZha01QVadrmjTc/y4VLO9y5d4+yqTk7Pnr+SYJzIdCdTu6zYewamnv+bNRqwLySdpwbZ0XCtquvP/tDm8axYKWUNEKBEAQiIEoShHACbaOfDueFsOiVKAhPz1drLXqFDbHaOKeWOufBgLVyPQQ+P4IgoK40ta3XTXiVfiZiiFh/PfG7+EowGFRMp2PiOAUMUZSAFR8DupdlTS0brAdNpUEbPCS+dGgQ7xkAO6xcTivXoJEC6SlCGSKFZbmYk2dTmrIiXXFBkWolsrukRaA8WH9GHcjYKoUnA8Iwpnex60oVzs7ottpsbW2sEhFOnLFNg1VifT2wAqRVLqlkBcZohFFYjItc/XS/uHsn/lKn0c8cpyrLDC+UrqbReAShhzSG8WSCrhu0qVF+gJWW2WxO0xh009DpdgmMRxLHVMJNhMIkxRhYLueUZY3nBavfo2i1PHpSsXewz2KxoNvts3Nlh7rS3L17j6qqGA4HJHGL2WzKnQ/vcfnKDteu3aApK+7eub+G9V66dAkhBKPRKZubm6Rp6mItQFYWBLmbwGxf2HRk9UBxcesiAIenZxitKescXdb02x2WyyXTyYitrS06nRa2KTE1NHVJFCZ4g4D5fM5kMqOua/KqJPBD4jimyGu6/R5bW1tky5zxaMKNGzeYzZ17pigKhhtb7Oxc4Y//6I+wFj735mfp94ecHZ9w8GQf6zmba567D8oLL7zAbDbjcG+fu/c+5Oz0lGGvz8uvforhsM/x8TGnpyer3DbUjZsklHnJfLp00RpjqPKKyWTCbDKlKIq1jdflHAOiJF4vkM5jVSdnpzRNQ6vVeu52xL/2Q7j+JQcAxCnhVuJJxxOwQuGHMSKbky8zrBX0egO3AFxNGKXvsbW1w2BjyOMnjzg8PGR7extrjWtoWgkfBwf7VFXN7dsv8uDBfebLjCRs0emmpGmMH0rOTkb8f9S9WaxtW3rf9RvNbFe7u7P36c+5Td2mquwql12AmySEJBAJ5QFeQCAhFAUJXkBCQoB44oEnJMQL8gN5AikRonESJYpJRMVxxbhsV6XK9q1bde89zd2n2d3qm9mOhocx9zqnKjZOmfhETOnqHt297z5rrzXmmOP7vv//9zfe4kWLECLIUasIlCeyA2xrMbZGKo/xBoNES4HD4b0BYbrIzPBQF0KQaIUhBuF2tjgpJU3TkMYxvjfeHTCyLCSiVU1JNBigbJARm07GG+SHcncAaZqGclvQmDYoc4RHCIkSCqUjvAv++jRNkaQ4E2yZbeNQAkaDIaWOmM6uEFIy2htSliUDl3IO/K3//W9y+vlz/vJ/9FeIo7RL3hBYOptb3NHhfwx+/Cd9We/55PP/mq++8+9S1w0UijyH6emCZboiHqx5GN/h7579bY5uvEs1kfQHMSv1a2SjkjMU68mMvh7yzt5dPnn8jzl87wa9wTok2zCCaIVrPcf7xzz/9PepOOV4KFmcnpGRcms45OVqRk8cM0okz/mUkwc3EOolxmx48PYdBqM+k0mNEC21ETx46zb9/ZTHL6ZMT68w8SWiOWC+2HLrSCB9S60tgxs1ymyojKKKDIUraYotvTRlNEhps5hENxTtZ6hmi05arHDE+TE6zdg2F6hxD608xAk2jlExVOWCMpZsFgUndszVdkskPXmaMIxSfFFQDFOGS8vqrVvM1lfcfHjIo8stZb3kcCi4tJ7IwbGO8CqlagWHyZibRzmnTz7lSDdkUcJiVnNeFCxWS+49uMuL7RXJWlGUjovVJbcORgCkUU4vKlGpRnvB8a09ltUSJ1vee/ddzs4v+at/7a/zCz/789zttSQC0oMB+V6f+WqLamraUmF9g/cNiUuJZPRHrKB/tpdSCYPRDS4vJghyjHEM9m4ynRfETlDOJ9y4K3lyesXFszk//wsfcrDfY1s4kizBOA0uoZ9bVstHbOuYXp5hhEAqwbCvSeMblHWLV5K//5u/SlltQ1rhXkbbeG7eeYtPnv1Dfunuv82N/JDvf/ZNjo9vMt9oDg6fMBopNquEhag53tvj0en3WG6nPDlv+MLtL3FZPeKiOeXzT16wXqwYDiT/8If/B5cXJV8/+AvEw0u2jeOzl89I431uH7/P2dXHXF0U3L79L/Jz7/4Z6mKKcZ7vfP9XuHf3be4d32c0Oubl5CUqtrz99s9yevZ93vnCz3FyfIu/842/zn/+3/w1Tu72uPiH3+bs6hErNeXe8Agdfc47jeC0fExjhhTbZ7Rly1xeUjb7SPGcpolJBpK6XtOUMdOLF/TyhNHwNr1en+lkzWYd4MKHe28Tj/eYr5esLksoHFl2Qr019NKExTri6M5dJuWazbYgHe3h6i2x6NgrbYEUUwY9waa2JGkPooysbagWa3oHB/gopRfFoD1k/cA7sQ1Cbd/gavRY6zGuRUuBd+zSOgH8dbFAaAijNNbWFL5Gi4jzxfeIlWSYHvLRy2/xM2/9a/zKr//HSKn5xQ/+Q1Ys+eT5d/m5h3+WUboXrIIetuUWKyTeGTId7xoV1zala2Cx9x7rBUkcowRIPHVZMRz2kTIEBYjr9MmmRpqWOA2Du+ufU5YlSSQZDk+I4jBUUyJEGk8uL+j3B6gownqBaS3OtWRxgpdw48Yhi8mU9XoZoLBxKITjKEXKoM4djUa0bUuv12Oz2QTruhAoKXfFWiM8q+kCoSSb1TYM3SLFwdERS9NwefoJBs+gN8A3DvfWQ2BMJKBar1E6wuHJ04y6rpBxTNkEtdBw2N9Zr4wxSAGClGk5Z7U54/jgrQBDXX2GnF7Rz0/4/Px3GOR3qMpl+Jy9xvvrYYsM3Bbx/yUH6ie/nINVaUGGaPVVWXM5mzPfljTO0RpDWZasqpqyqanqNqSTCokToaGnlaJxYYLuuobfK0WwQPxY3ZVohRSSPElZN2tcGxiAWRY4MiGBLdwPQiqc8wgJy/WayWzOWwf7RFoRa0h1sLSEqGexw01sq2DFNyLBdBbCWgWGkkp6pPkIVMqN+18gGt/AqQiVpjS1CYNz60gTjfSOJ5/+gB/88CP+6v/4y5xfnqO1pOcDfkH3M0ZpShxFJFIyHo2IuiHeaDjESIjj64GdxzZ1qCPwHX9FUZuGWCmElkQyIssy3vvCF/nwy1/m1u273Ll7n+Nbt0NqVVvSNhZjas5enLHdlkRZzsntWyR5D5KI1rtglzEGlMTjsU2LM5Zt3aCsp3aOJ8/O2Ww23H1wPwyNULzz3of8J//pf8b3v/99fvmX/wc+fvZmFbNAt/eFoZX2QW4jRWjXBHZmlyIlZBBvCIHD7xhI3oPs+J4Q7i0hJXXVonVKlmb0+0FtEthhAiF0N2CV3TBfAgFYriU7O9SmbWlNOLM7GRAG15H112q968Cepml2BVAv8QAAIABJREFUrC4pNLqO2Gw2u+ZynncCBgg2N+/RiQbrSPOIug4Ikmv2l5SBE7XZrndWzlq54ATwDU45UhWRCEXW75FkKUpInCAMqIXEOUnrWmxXZ0gloA2pz7YokAhEnhMnKY7gFhCoMHSXEilk9+9oN6jsDcckvQGDLAnW2fWc2dWEH/zgEwaDHidHN1BJjNYyYGe8J/Q1Q3KckgJhWpyQIEF6gSNw2/641088Irx1coLWMXW5CTYdCVW5pW1qnO8SAoTD1RXr1Yq6DF7b1XKJAEajAVW3dedZj3JTUBU1ZVEzHo3QSUxbVLTWMJsFcOZ4PObh/ft8/uw5l5MJZVly+/YdoihiNpsxnU5J05QPP/gSg1Fosnz88Q9ZrRZ8+ctfZrXcdFycjL29PS7OzsmyDAgLdnJ5xWg0YjQaYQeGOE2om5DM1NRb+mmPLB4ihgrvg594OhX0s5RemuBHY6R3pL28S/YJ05PlchOSUbpuf1nWrFYF0dkZk8spt2/fpmkqptM5caLo9QY8efKE+XzJl3/qq3grWKyW/MZv/BZf/9rPcnJyi+225PzlGQ6PjAoAFosF9+/f5/0vvs/lyxHPPz+lKApQknv37nD//j2+9KUvsloveP78FAgL/fpw0jQN1bag2JQUbQ2uJU40w9GNEOsaxeT9AKIq65Krq8suUQniOHqNsv9mI/p+kusPVeF4GbrZP8bMEajAxRGvuvsWjzENWmjyrI/xQY0TxylRlFCWNeNBxMnJLZ4/f87p6Sl7e3scHh5ydXUVpnoxO1DifDLDvfUOp6enYUOMlsTRCd4LtqtVB8mKiVQEkUChsa7F1A2VLMKm2HF6AIxzSGewEpQN3d6dT7v7aJwPk8/W1Ehhu98tJc+hrkvSNMDF27Ylz3roXGNtABAKBRq9gxy2BLhX04ROetu22LYkjlOUihAyeEnlNXgY1wEkg1fUudDgFQS5aF3XNG3Vvf+O4WhEv9/n6uqKYd5jMpnwj3/n23z3O1/n53/hF3ZKISEUWovO/uVRUfJGp83egasgl8dsS0OsJN9/+lu8dfM2i6lFrOZk/StWkxrphqhhResnDEYJg94hxXJOuZQc3u2jBoLn84J0TzJZPUOnjnnTkmae7foROqkY9I5Ja8Fifk4ceYq6pGmH+HKBEJLpekn/6Jjff/od/oO//O+jR4/4wUdPGYzf5fGTj8iHFkuB0prZbMblswnzUoKGNDNU6zl1K8ii8DCbzaYcjGMW6yU20WRJRt7WaFpo1tw8uslscsmqPudGlCNTjVaCKAPVA91qUqNJ85jecMSMYBnM+/sUWwPChcmQ1+Q6ppUSZIyUDtNsKI3hxfnnyFjTS45o5SXjQZ9+ltHbO8AZz+XlhHjvmN7BITZS4CX9LGcyWyDTDKMEWZaQJIf0rCdqJaNhj8Y4fJ6QJn2a5ZaClr2xplY9sn7EvJhz58F91kXJr/293wNZ8y985S0Ox5aD4S02y4JptUAR0Ys9vTRju61D6pozWF8h5JvdF6WUnJ6e8tWvfpX5fIoQHseaoq2D5blqkEtJVdV85etfZLqa8/L8OTdv3ube8ddYzJY8fPtD1ssZjx6dUbuK97/wReaLKc8uXnByfITWAh15+oOM02fPuX3niKYxVIVnNNrDO0G5mWPcmqQnuXnrLdI0Zb25QukBTVOyLQq+/d1v8M69D6lqS2EWzC9mfOvJgGLbsvUF+XgfoxPirGLz9IKsN+DZy0e8985Dfu9732W+mFOWZ7z/wU0uL5a8+95NTsYnfO/3/i4fvPN1np09prff42r9+8T2K9y+9SV+46P/hcV6wsef/0+8fHnOv/Fn/ws++vSbvP/lMafPL4hiePLyIzwN/URTNxs0BpQhTgReSap6i5CKzXZBnMB8UqFkRlmuQThunByw3jRY62kbwcVqgXNQVgv2jm6yrS3FvGS9qRmP93j+7DHr9Zp+OsQ5x3g8pt4GILKpW2SlED7DCgeuCQwWI9BRguoUsg5PmiVIERQZ1nriOKWoFmRZUCun6tW0/E1cvtvnZcdOCwXUq3SP69civXzFa1GhMLZuRawSQPJi8gknh1/kH3z831HU54wHDxgMbjNdP+Mrb/8Sl/NTvLgfClhbo6RkmPVI0xzb2h+Z6gaYvidSCqEFqUzpx4rmmueg2SlglBA4QnHqnMFat4ML74oXAXXdoOMMFQdVTOMcaZZw8+QOi+WMYrNFpxk6jqiKLa1OcCIwqA4OD6nrOjAa9DW3xlM2Jf1+b/f+Cf/awCVNd+mpZVmyOL9k/9YJTRXAstv1hv7BATiPaTzp4QlmvWH64pThcIxZLKgN2C5mXceKJEvDP0lCY1qcMcSx/BF1g9aa2tigALGSe7d+hunyU5Kkx2h4H0HG6fk/II2GrIvnuDaEOEihca7t/hzYHt77NzptEUKh0wFPz664mi05vZxStgYrBEVVUjU1VVXSEM6zWd6nMcEuUtRVWKvWoRGdVUPvUAp/2HXNnby2ebvOJZPn+W5IVpYlHoOUGtOZeubLNVfTOeahYTToMRzm5GmE1KKLi49BQlXXWKFojKd2EdHggEgpkkEAxZZFg4gykiSFqBdsdM5xdXZOtQ3D2nJbYJuG73z7t/m/vvF/Mpld8ez5U/JBjnUOUTmUVPTShF7WI9KaVClM3WAA4x2T+ZzKteguzjmPErI4DICNbdgWBa1tcAIGKiLREbdv3+HWrTv8mT/953jn/Q/pD8cUdcN0Nqd1FmNalssltjFkWQCD98ZjRgeHOCmo2nbHaZEdF9J2irm6rNjMFqynczbLFT/4wSe89+EHPHznXYSM2GzXrMuSg6Ob/Nk/dw+hEn77d3/3DazCH72E9zsRxo45gw/qEQImyfnQ7DbeEgmFQmFEExQ83ofvEsFi5BFonRJpxWgwQuqI6WQenC1lyeHhIf1BilZhqKu6+9p0ShlnwWqPiiIcjqptUcagCRwz7wRK0e2jtmtyaPAGgUDJCKECD1R0z6GiqqjKoO7LevmuXkmimKLeEsWK/YNxwDCY4MwxpkEqdg3k1WpDW1QYCRpBrCN6WeApxUoTqQihO05Q7XFKQgSp0BijkHXT8XYiNMGpEGkJ3jM+2KO3t4+bTzHOEikFTiCUQqsI1e2RQsdsyiYkFDtBaxz9/j793mgXcPT02SnOOY5vHLB/cCMMy5sGby3OWIwXyBAbFGpLZKem++OvoZ+oiaOU4vDwEGMaqqJECouwhmJbhiLX+e6m7TastoEkwVtHsQ4ThCzLdt7RzWYTiNZakfVyesOw8SyqmroI/sbj42PyPGdTrDm/vKSpKtIkWH/qumY6DclQb70V2DXr1ZqqqVksQmS41prtdktRFOztHez+3uvX0TRNmGYgaKp6J40syxB5rJUKD7g4wJTm8znChcP6NWleKUGaJMRxHCDB1tMaQ4Cd267BISjrkqKuUCJ05/r9MO2ZTCYcnxzupkPbTUhfuH37Nm3bslgswvd0trCrqytMXdJUpmsIlCyXS/rDHgcHBzjvaeqapgkgXWsDX2Nvb4/NZsPl5GLXxa+776u2Bd52XcwodN5lFymplNgVzM55qqpG69CFPTg4DBtQF3H5/8dL/BGd0FdpFxLvFTLSxGkCrcfaAPcyHTtGSt11vWuiKCJN012DyDlHnKVkvZxisw1ybymp6iKAwW3LZtPHWs9qtWIwGu2geUopHD6kGsiwRltTgxO7xAlrLU7SSZ4JKhznumbOq0nk9aFjx3JStrufWqLII9Bcx7UqxY5VA6/iMncg4e5Qe83BuUZmKqUQMqwheQ3h6xQ+vGZVkzJspNYGFR/Q3UMttjVEUtHPe10al6PYbnny2SN+5mtfI01zvO8OhlKFbgpBMv8mJ3xSaOrCIeyAWGdMLy4oyjmL5ZrFMsGrC0ZccrT/gP7egG07RWUKryqKYkMU9RG+oRUtSIFOPC/PnqGiGp0LrHTUpsa4lrouwDUcHdyg2JTs7Y94srigqCvuHh3y+UfP6A00RVPz5Z/6AlfrCbf2w5QhiSXjg5zp8iVONLS2xZShiWscNFtDFNd4XeGcQkcZIg4HOAuUzqF03AEWCXtQUzLME6Qw4DVK5iRKEkdhKhNFEdrH9LwiEiHBQwsLKBoZhXhGUQd4pnMBMpznoBXeSYq6BGvxtiTyPTCaKJbkaR4KmKall/Y4uXmbl9sGW9UMEKR5Fg4amuBBlgLTtig8RduS93pEiUZFkuObN5ifrTgc71HVcwZHI5TMyfoxqABGrYsyAE2rikGW0+/lSBUOO7GLSNOYPM/IfMJqvgIZpLlNG+DJb/LyCCKdUBRheHF4eERjN8wnL4i0oMWx3FaB7dZLKNuQkqh0AOduioKDcZ+jwxN6eZ+nLx/jvSDP+iGJ54gu3rh41XTtbJh53mfQ3yOKFAu/oGxXWNcQ6Zyq85qXRc1ms6EoS7wruZzNAUEax2gBk9Vz8vgEHStmyxm2bVmuX2BdQy9XNK7k9MUp09WMo6MTimLDarWgl+3jfMV88Rk3v/RvUZU90nGMZUTtJphmCfQZDvfZNi+x6W8yGH7AcvmYs2ffxWpJvUoY9IZkyYZtvcW2MSUa5xVFKamNQnRFvtIJxjmKsqRtPNkgQ0eO1pRUZcO2WBPpDCVj0jRns9mEIl0rNlXNqBfOO1kUzg3XNp0s66GihLoM5w+jJKY2KA0ITWMqXBvAlcNRTJrkOCNwrcAJj8O9lpCjEToARyWKcruBNzxscdYjhO3YAA0CGSwtyu3gsLshimhxLjR2pRR4XxOpPbK0z+MXv0qzbTg5+hrv3/tLvJh8Hyk8T5YvOBo9xIiGNnBKKY3lcDCmNsF2G0m1s0gbF1Kskl6fWAq0Dg2coLZpGfT61HVJrHVQmlobFBJC4X2wnVyrWKMoCurmLmkIL3fT5+0m2NGVUgwGA6bzGXnWJ037IbmPFufCvZQkCcY7nLEIH8CceZ7T1jV0DY+6NjS2RQlBUxRkWUbdNCynM2gNbV3TFBVNUYTXm8XgRSiIfBcXnA0wSExV4tKgulMy3SmunekSuXQSijNea1YIUJ3VwzpHnCj2BnewruT04vfI4gHnk98ijgZYtw3Wte4M4r1EiNBcf2VXe7PrUAjBqig4u5xyuVwx3RYIpSmNZdmduSOlyAfBgmONpzZBPXBtl7ru10gPOope++8/Cix+/boGGl8zT5RSIcG2s0b+iI2nq+jrxux+dqxjIq2Q/tXfcT2cswgsEitARCnpYI84Tkn29lBK0YhlUEZkPRbLNWePn1C1DZPFnGK+oCgKFvMpTV3y+9/7Lr/3ve/SmAalwmfcNA15nIKUOCGprcP7YD8Lw71wtiiqkAjlGoP0YPIcMRwSKUnVNBRtHZTgaYp2ofjfH+9xdHBIlqQ0Vc1VfcWqqkJjyHoEoaaI45iDgwPSvI8TkrKqQGsMwX4mO0Xa9dk0FoosTlCtZfLsJcvFAqTm9p17JEnWJTclqCjqapmI9957n16v/yez8P6Q63otXGMPXm80Cx/sQJJgNbeq3Z25rXBEnTXHmJZYRSAcTWPI0iTAfUVQ1jljWCwWFNvt7jwWztcG5wy6U265a0CytRgjyPOcum7wIjTFVBJ1jdhXzKHrxq5zbsfQvH6GgQ9R5N3ebozZCSd6vV7XJ/DAdvd918luYS/S+C6AyPtgD+srSeMttml3lrMoikii+LX7S+Cdo7EWlEZcDwmkBOuQ0mGbYAntCETESY4xLSLW0FpoRQBH+3B2lFJinEdLQaQSZtM1aRzOfBZBoiMab9gbHxKlEdW2YjaboHSwyGqtUCqmNTWmaVFxHGyR3uCdx3j/TzRipJT/1OX0T9TEieMAT/Od565YLljXNd7WpJEKzINtQbFtKJoK7+Bgf8xg1KcoNp23MxwijXUsVkuQitHBAbeShDzPA3BoMiWNEw4eHNA0DdvtlvPzS5QQ9HphMvH06WOapqZtDffv3+fDDz/s8ugNnz4KVqp+v8/+/iGTyWTnZ76aXVGWNcYEKWHbtoz7PdbbNZeTK4bjEdLKDkgcv7bgGsqqYbNd0bQNg2GPXp7StBWz5YpEJ3jjWa/XYUNxjryXsFgsEajObmI6n6KkLGpePntOmqasVhtGowB4zdIQmX56esp7773HYhE4Ks+efU7ZlDy8e4/lcsnFxQX1aoVrDen+iPnkiuVsyo2jA+7fu0PT1BRF2RHfZ5RlyfHJEffu3WHYyynqitnVhNVyifOezaagl+VIFEmWhAQrL9lsNrRtd/juphGDwYBeLxTWw+EAoVQXb/nPX4nzup/9DwId//j3vgKK/ZM8n+uvQeiSBy93mNCUdYG1DqUMcWzxdc3x0QlJEjGbLTDGBH5TJINNri6QKoBEsyxjOV8QxxHeB4BgtS06Vk6NcY6mqhiXJcPhmPFYMOz1KWuPVgrhJWVT47pYyrZtXwMNJ+A9UoeJY13XFE2NcK/eB2uDpcm5IInM8z5Jkuzejzi5frA4vOsOX911zauCsHm3dUNryp1fPNIRUaRC409HOwBgsJ1GIW5TBduXBGq7xXavM9j2UlwTEiPKzRYbxyQ6Ym80Zp5NWBdb/tGv/wbvffBFPvzwQ5IsNE3Dg88hhMS+YV+fEB7vV1ycvWB4tMfjR9+iTVrG7+zzzW/9Kn/xL/0Mf/N//t/46a//ZUq55PnyVzh++IB4WPJyNWGYDlhWDa5acLW55GfuvMVnp0+RWU067LEtLkjiDKlGeNmnFhv6yU18MWNw64TPlxOevzjj3Xdv8c7JLabFGX/mL/55VosZp5uCyfd/iGtzXp49Q4oBRdNQmy1plHLj+AhtFAdNy0cfr2l6G3oDSdG03BikKCU57B8y30oaUvoqJY8jWmMhCUqu2IdDqWoUwkUc947IBzFx3kepnLYtiVyBaD3FZkU8ytBI1q0gToa0vmBdrKlMQd4bg5TUKkC6J9sr+i5H1RNyAXHZJ6ZmUsDJjZvYraWxmvHggKHacHRyl+dnZ8TSc3TziM8mZwhnGA2H9KaSTCqelnNEP2Zttjz8wls0tUFMN+wnGfFRn5muOTo64Wp2wareMCq33Bjukb0XsZx5ik3FVDbIaBlUiHmGiGLSXg+7Nrz7hbeZTC4hgiY1eGZvdD06Gw5VFxcXgRmXj7GFYDZdc+vkhLJtuX90QiRSHn/+CXfvfMgH7/U5O/8UL0/p98ZcTS+ZTaeMx0P2+uMQodluObl1m5s3b1KWJU1T0Sy3PHjwgIvJY7brhps3PuDyYsZ4b0jeO6Bs5mzKiqrOu8lVwsXFhO12i7WW0WjAo+ePwmHJJdiyZlpM4SBnXa0pS4i0J1LglMT5mrZd8NnvfsTDhw959niN0g39UcO/9+/8V/y9b/63NE0OUY8fPv9H/O7Lv8Ho+Bic4ckP/gal/4Qo6jFbTlnUF8xPLeWjGaps8HuOZlFievs0TY0iwacxMs0DOLOusVIgWodWCRBhjWGzbhBW4p0mjgbgI67O1qRDw7ZY4GzEoD8kzSKS5ICr+ZK8P2Y2myAVVDYiTXOqqqKugwKyLEuk8ygNLk4RomsmGkHTeFxt8FFCKyOkjZAtRDZBZRFCWrJ0QGu2KG3xMqUpa7QMVi/8m0N4eu9o2hrtNQFA75FCIYRCAUKGZyAQOCMEC25QaoTi2Ig1zy/+Hm3bsCof8/UH/yW///RX2G4m3D76KnGUczH/hG3VYxmNSaMes/UFX7j5kO1yFopZ79lut8H60R+E57qUgTFH4HZdFxObYks/zzBdGpRzjl6eY7bFjj8D7AYs6+Vql0gazh3h94njmMViwcHeHlVVcXh4wGq5pSznSOFRKqJYLJCjYWj2IGi6wrknc4qqxvmGui5ROkZHknIdLAqD0TBMeAWk/R5Owvz5Of2jfar5CnopL3/zW9x45122iylNseLGgw+QWuCVoJjPqMqSKO3TG/RhDlESg46Ik8DksR2XbF1s0eJVtLGSCZGWjPoDfvDkY1I1RqMoqgvwBqlijHE44zr+g0epGh+AOAA7JuMbBRtby6efPeXxi3MqC42MWW4LDAqZ9InSMIQubEVdbCi2FXmaolWEJ5ybBHLH9LD80edMJUKCr+n4nnEcsb+/z14Hs17M5nj72rnZOYTWtM7SdNHxSRKSRCMt8VKilKCuS7wXqDglzwf0o5x4cEQ82EfJiKerDaurBVmUEzvPYrLim7/+DX7nd36b5XrB48efsVzMQjqoDqBcZxo8FmtajIJ6W2MlVHV4Hd5Y4i4ZNFj5QkPaqTCAGamkS00SOCGo6gajFUJHjPqHxGnCYDjkwFkGvT4P7twlSzJePn/B1WSOjBNEljM8PERqxeHePr1eP6i+G4v34Z5rpUAKQX8wQO7sNzJAvYG4Fxq2sxfnzKZTYqX5l37pF7h1/y6IEM2d5uHnlnVBUxvefetdbtw4/hNfg//E+nBBmW4FO6i4JyhshBAIG5g1YZcSeOeJhOj2SoXA07QQRwn9/pA4ThEyQTqJMY6L84sQPnB8k+F4QBQpGtNiTU2cZzgLTrgfQRA4PFESB3uSDQlaWimiOKjyQxiM65wc4f8yxgGuuzc0hSsZ9Ec0TUNjHPujwC4qNkX4eVrjZGgWKR0+3+FgTNM0TCYzZGcdM+a1hGAVBh46DXtlGoXa4NrehQsgYa01Gh9A3z6gQ7SQyCRGCY8VHtmVNDpJiQcpXktErRBESBn2b+0FTsqu7gsg58l8w2RRMJtN2KwW3LhxyMMHdxkMesg4Jh/sMxjCwa1bXJ2fsV2s8M6hkphRL2dwcIApa6xpuoZwePbIaw5S9+wIKsU/ASaOtYa6arlxeATW4JsarSWx7nFxds5quWC1CPT2um04ODiin+UksSbKJM63LFczkm4CdfPkLmW1Rnjo5RnbzYr1Ysnh4SF5mrHebjg9PaXcFkFThmG7rUnTnGKzpmpavv71r3P37h2ePHnKbDZjMBgwm0wZjUZ86cMvsd2uefHijDSNKeqKoii4c/MWXEvBpOTs/JKmLsMUPI4Yj8eM9vYwTUOUpdTbgqLYMp3OkFLS7/dBSqqiQEpJuV2zsYvAw0lCksRqtelix/oUZd11E/VOreG9p9wW1HUNyzlvv/2Q1hqiWJNkOU+ePOKd+2+HA/KLlyiluDw7RyN4+NZd+v2cjz/+uIPaGlCKSGum8ylVVbI/HrM/HjGZzYmkQgLb1TawbdKUOAmx7nGcUm4LIhWH7qR3CCcRXjIej+n3c8qyZDqf7eTDQgju3btHv99nvd2wXi6pqvKNpwK9fv04lOz/rXnz49d1s+ZH1CGvfS3EjL5KPVJCooQOXWyd4IxF5RFKhOg8IUJsH9KSZRnzeYghPzzcJ1aaqgoxpUkUfKNVtaVuynCIL0NMqoo069WW3nDB/TvBx1kWAWLYz/ohNcKEh78X4LqJjveeVrWstms2qwXbqqQ1Bq01aZwhVNjw0iSQ6aNIhCQMJcnzfifVjbroc0nbWqQKzRtvHWVd7ZIxpHzVjMnzHKkgkmpHpZfB0tt5eh348N4J2VmQuI7kDPci3dQrzTLKKkz4i6LAtC15kvLF995nspjz8aNH/Orf/jtslxt+/k/9EkqGz6/1Eg/hIfcGr7bZcmP4p7l8+Wt4jhgevI8ov8MPPvt17t/e5zvf+F9578ZP8X//7re5qL/H1z54SCXPkXHN/ZO3aLYlX/jwa3z/k29xcNyiE8X40LJaCxKVorKGuq2Ih/v4OOJmepcns29TGYEub9OLh7TRhkeXa3767Xept3f49Hf/EU9nz3n7vV+g5i4m2zCvLIuLx9TGkY96bLcl88snfHDvfXo6xkvHo/lL4rzH0i5ZL9Z8eOuA1eqKJhkjk4qj3k2KeoXMLZlNkUgaXZP1Ko6t5Mh4xnvD4PXfWGS6RVqDEglRP6US0BcRDaB8Q19LrJQs6jlDGZH3j6mlBKmphWbs+iyTFWm1Ty5jjK9oZIRyhrIyjPt7xEmPjXPE2YC1qbmxd0BVVcy1Jo1yahsmRL2+5mg45JuffkRjJO+kt9guCmyvT9pLUcrQOJAqppFL8v2E6jRh+XJKczjibDoF68iEYrtpcLJl78aC/o2fYrh/hF4tmZYLVrNLatewd3DM4xef8GbJDyFpz4uM8f6Q8XjIDz77GNpP2RuOaKsVuZZszgqiRtEb7rFZVVhluPX2F3CVpNyCQ/DBF+6yN9zjt379N/nKn/pXeWJOmVDw/MUFWZZQ1kuWmwWD0QhjNFkv5Wr2gv29m4G9IhqcTYmSjOXZlCyLsB4mFyuUj+iP+6y3W+LcU6wk1daQpzFpZqnrOdJKcCVF2eKEZ6QkjQ0251sHIyZPn3B4t48XOULc41f+/i8zHp6QpMd878nf4sX0u1zNZ5CuGfTHEGu+88k3OTjY48P3vkzVfMhxYik+XZE2MZfrK1Dw7PlnfPmnfo66cjx99JJ635CNY7a1YRT3Wa9rWrsiiiV1FRSaTkhmqwtqOwgsir5C+Jg8kxgrsU5irWRTzfGEfaqoLXXV0k9DvLvSGZVxlKsVqJo0SVAuqCJcF/csaEP0cazIoh62CgpG1xriSEAZALQi11R1S3+oEEZ1ikjbTXPf8Hr0DmdCgeWiIGl3LtixlQqMBgg8M4QF3yIkaJUHJRGaxl7Szx+AeMB3Pvnv0bJHOjhhvv6McX6Hwl6wMTm+cfzrP/1XuHvwEONaCmNYry+JVQq+JrEaKQbhuSYCoLeoa3qjEa01tI0gy6MOvCrwQiG9p6gqbBsKlaIq2dvbC9Bb74myHGPbHSMHrhUakEWBj5gPR2y3W5wSZCpl8uKcKFYYZ1lOJ4xvHNIfjJh/fkmcJCRvD1g+v2A+mdAf7pGOBEXZYltHlKQ4K7sptYXG4KwKBXmUsNWevV7M+z//i2wWE5wzxOMxq8UkJHMpCakAHeGwlFWDVQXKGnScYFqH1Io4CxPukHAqUdfWbNvhSyS/AAAgAElEQVTSOji7eMmN8QHGGR7e/Aqz9e8xWxTgWup6i3cxIHeMXWdF4Ax1PI83fZXGci4UE9Oy3RQ0xiK1Zjw+7Nh/DVXbMu9AzPv7hzgTrHheqi75U+N0aOw1nRrr9dPGK5ugJ4o0+TAMnU0TVDWJkty/eROpFavVatc4MZlD15bMRzQWJrLmrLlCtytOxIhR1iPKR2SRYt1Cm92AtEdydJ94eBAUsrGmLDZBTTlbUEynRFLy8Q9/n08ff8qq3TIcaJazgu3ZBcQ1BzdPGO0dUFQlk8WSq6tJQAT4mMQrvPFYERT41vudguh1e1+mo50KPNIRWqlgdzItEY7YtPRlhrI1qax490s/xYMHD4iTDGMcSZpx58HbpL0+SZ4HNZ1zpHqIIwo8q0H4mSrplMAqIklTvNa0bWC5mHaGVhbTViyvpnz7W9/g5uCYB3cfkL57Hy89Qgmk9qDC+TWVMc5bVuUa9c8pmEU4j1QC21mrRDg0hwGoVCjvUN394oIHB+eDAl/qjN5gGNT6UYxpWto6KFpOT5+S533efu9dpKQbrAZbobWvUAQQgkWEE13ylQ8pfXmK3Ri8ENjWgAsMF7PDNNgdN/W6qaN1GP7SuRGcf8XSSnSEHA52tUOwAAdgd2WbTpXoyLIeprV4HFq9amh4b6GrryKliZKYNErCm9gplwLXR9HPIixipxjTwqNUsJ3ZSOCFQ8URUS9nOBijo5g2btE+DBGEEBAptA+NNaU1TkjOpwtaq9jWltmmYf8k5fT0kqIp2N8fcvvOLfJUkyQRhzdvggXpHI1pWa+XLBbPieOAYUmTDEdw89RNUFi+qj2vMdZ/9PUTNnEccZThjaVqa6I4AHy8bQGPs76Dw0qyJA3TDG+x1hCLCEGA4Wa9YejYRZJtscZbQ1M1rNdrnAg2I2MM0+kUXOjaLZdL1uttWAxZjzzNSPMex8fHNE3L+fk5o9GQxWJBpmP29w8YjUY8efqExWLJ+GAvxIWp0PFqfXggX4NmdRzt7FHWdr64zmZlcRjvaGyLcILYpqRRwnK53EVS1nWAeKWd1zDIFFekaUaaJLvo9CiKcEik7+ReQrBer7vmiCeOEshUiCo/qdjb20N2r00rxfTyinv3bnPr5jGXV+c7G40xLULAXn/4mn1sj7aqWVcVB4f7gZ5tPE9ePEEpxZ07dxj2B7uGR11WO1mb2QZJZa/X29mDdjdeJ5tLkgTnggS/KMqfZCn9M7muX/fripk/6Os/6c8Lf94NjXZfu5b4QXiuBHZMs5sEuNZQ1cEG2Msiiip83lmWvRb96VBKEsfJTgkGAuvBW0vTVDvbkXU1zUZRFAVus6EoarSKKXslw95wtwlGSlE1zY+81tbUrBdLyqbGA1nWQ2tLpEBK9ZoUEsBhjENKvSPWh41L0DWlu83Q7iSP117vWMc7u5fSAi3FjplDN2kL3fpAgEcJnDU7Xk8cx9DJ/S3X0zkdbGhAXVVB9hklyDSkpJnG8vnjz9nf/z5f+epXGYzHXaSfxVmHf8NpQN5ZmqLCtYLVcouTa8bjHhdXc372p3+e1TTBzzxXz7/FqpmDuo3SmjjJ8VYSpxlOK1pnWS3XtK1BR46s5xCywZSG1hiitEbIIGPfvqxxXmKrBdYURLGg3+/TG/aIREM0j9g72EcqT7EucMLgtwavJFKEzzOLUkQapnrHw4yb+yNeTF+gioo8BqyjXNf0ohuslxJpI4gFrqWT4kq0EGFigQgJTs5hbYuMEoxtECYwHpyH1tkwSdQKbEscp9imJMkzaKfskglUFuCLMiLSCUKBkuGwkCcRy/UGhKA0DUMZ7I20hALEgdCKKE2YrqfEcUSMx5kG42FZbEgGPXxTYjYle3fGTK1FXKcuiMCWioXGuLDWtfc01RYZB9ZUWxTEaQ8keCcYDofEOuJ8tUJoRZb3Gfb7tNKztz/CmPM3uh6BTsIbsVisqCsTwJU6pipD2pyMIpxtaL1Htg3CGmwlcFaRpBGLxZLlusaYhiiPEVGw6BnTBOZ7TxPpAOsfDoddsgSMBoNOdu07q67Eeg1eARrnAwhTOEvbVngtweugHpQa7wTeBeifkCClCF5/AQaNUglKO1arDcuNZehHJFkfqQaUraIuFcP9kLCzXm2D7bSOaCKoqoa29USRoC4s3gt6yTFpL6OqtqyLNVkvJ04TJvMJQiTUbU3sAjxWa81qtQmqjaKkaT3GJCgdoyJJ21a0jUXEijjKSSKFQFF3scquMTvLjacrDrE4G+zWWscIgjrSywhrOpCjlCFtw3mcN7szy27/8bazs3mMrbHWUVWhOeKdQjmomxarZCfPf3NNnNeTIq1zSGtpvCfWUVgP4scUsTKwApw3NHYGRgVeiIjYVmccH3yN2eJp17T36Chi2D9hqBWGiMPePebNhiTSKBXx2dV3wFlOBnfpJWP62RDpwbYNLoowbU1TFt09k6DzYC+NtUKqV+cdCM98719Zo6+fc7H3WK+CeqoDvV5/RjLS1EXBahFUe8LDcrGgN9onzoM9odmuWV1cUU5WCKlQecTls3Ounr2AyFGcN1TtAByk/UGX+GVQWtHWDUIKojymqjSXFy/xtDx/8jn3hebs9BH9wz18a0PgyDUEtSxIxn1c3UIUijeh5Kv0MOdDo8vbzkr+Ku3S+w4XYCz1dk0rCtaXP2S++hxjLVKkYU07+Vqajd+tvVCUebqIpze3FoHpdBqaJ9aTpBnD8RjvJXXdUHeprVEcvUrn7Jov1+tAv8Zy+sPOnRB+ret1sLsHeDUgvB4Oaq1R1vAH2flDGq3cvffOuYCsUBFJnqHzIXGeo9MUIRX82FCs1+tRLua8ePEiKPFvHjGdzzDGkOcpB3dv0e/3aZzf/b7Q8U6sxXQMIOKgFkl0gtBq95rEdfKWlEjvua6nnPeB/ePDXtbiaTvb5DWSY7y/h5JR2INHe+SDPlGcIrUmFhLfnbdFpBFS4rVCSwFKBtWvjNBSYQQBhmBsoFi2DVcvz7k6O0cLyc3btxjt77GxhljFKCm7xKvusxGdotxa3Jt2EvhO4d85Z5xkZ7nbYQnwgSnpWzwKiccR3h8vQh0RxTmt86xWJb5tsXXVBQdljEd7aB32LWMcVWXQESRJEBIEyoILlBYpQiPHOyKtUbHefWYOh/Ae8yMDgGCTvFYmXt8PkRSIJAnWKJ2QppLNZo3pqPbX9VKwz7d4CVGU0La2U/R0b48DkN0W4XYWWx18xeiujrmGLLfeolxQyOECt6oLbwu/q+sYnTqEn1wDnHUS7+LchQqDeC0kSkS7mlDpmNo1oDT4mMlsxbZoGA7HiAjqmWO1LnGnz+kPehwfHzHsxUgdnhvKBvWelJJiu93VUXGiyLIcx6vn+jW37Z/2+omqHa10t4GBtAIdx3gfsV5UNE27kwM1pmGQD9FRaPA401AVq66jLaiqISrSQdngLHVdc3l+xmjYZ39/n9ViydnZGYv5FGstRRUW5aCfk6Q5i9kcHUe8//77rFYrnj59SpIkaB0xnU6pnePg4JDLy0t++zf/MXfv3UaMfNiQvePxo0e7mOz9/f3d5mVMAzLYMbZlyXA4ZDabsdkGi9RwOMQ7OqDyJBSUziG1Jo81bRksWteNmcvLS+I4YW84AqXp56Eh4vDdpCcUv2W55fLsnP6oz3g87gCxDZeXl9R1zTsP3+Lxo0+JpCLq5Tx//oKbN2/y5S9+kU8ffcZ6uWLQ76OjkCLV62W0dcPZi5ccHBzsHkDXzaLpdE7TFkwup9y7d487t26zWq344Q9/iLeO0XhAFEW7ZpCUkjzPO79+IIgvFgtmsxllHaYR+/t7O5npm77+uIfSH2/y/EhE3Ws/MniegyXIudAETNPwflRVufODVtbiZeDkBECw2x2AhsMhwddeM+j1aZKWqqm7B2pQO6lEkfkUa0zwy3toTMlqOadtg+1KyRi90lzwHB330ELuouDjOGVyeY4QiiSJwqZJeA63XZS9kjFpFpOkEfhwPwcGVE2SJLv1cp2C4ZyjLMud5/T6EKKUIomCskwphe7kj1prvA4PVtwrmbEUHn8NM+465nEUNlFvXQA0l2BsjWnD9C/WyU6+3rYt8/mc9XoNQFXUfPLRx3z6ySd89ed+NsjvjUUKgSnrP9Z6+ONebV1w9uQJ45PbzBZXpLc/48nZc8p2xePPPqKqT7k4vaBxl2h1j9LN2RsIjB/jG0uzNSxmU/7cX/hXmC4/5/y0Znizx8GNhNI03D64x2qzZN2uWG5aXoiG1aYNsd3FKbf3IvrDfVbTOdt2zbpYM9jvo3WPfKAofILTGfPlgm1riZ1FtjX9vYi4F0Hasq3X3Ni/yb/5L3+RrB9xvnyM4QxzteLhja/z57/+i5w+/RyXFLycPOJiPkOKBi09orUkUQxxzKre0m8boiSmtRWmkaSDwMlBS6xwtP7/Ye5NmizJzjO954w+3CluRGTkUFkzUCABECQaINEkoe42SaattNFOK+30q7TkUtamXrS6RZHdNFKkSJFAgSAMqDkrx5jv5NOZtDh+b2bRSCPRLaa1m8FQmRkZGeHhfs75vu99n3cgBkelS6g11ihW6YqyULggMHVJH3NTxpZTXHjOZFJipGBZz1ldXeCnnmgs1FMGZdn2LUtbwejbFwZW2xWLagJKsLpeU+jE8+sLXgw3vCNq7k2mVIWh6BOdCNkiiWWyPGV5eh8fAu5yg+jWGOWIxwLfempbYZRm1w/cvfcQgiZ5wWyx5MX5NdVpydOr57R9RzCOV+rt13KF6CkLhVE6J9wNAjGpWRyfsdsIUlTcrNZUVU9wA0USTFVN33ecry9ZHgdUHRnoIHhiZfh3f/yHXF9fMz2d4Z2jKqe07Y7gJDc312xWuZl99GCKIANXXe+5vV2h9AwtZwxtYrdOCKnx8ZZ269H2lDh4iJayyJy6toeb9dXIOQik5HNDMSzQNjdonEjMz2qSX7JZ9ey6Jzw4fofo4PzyC8JwjLUlNaDFMSJWVOURQkSadsXN7hLnIydLh5nM6VNE+4pdGChEYu2uEaqgXhi60LBZ9YQhEpIm+B0hdggSSs7xgyDEAZIhBkXfZW6APD7BapNh86N9ZFbXJClpG5ftOuTo6xTK3AC3bjwXVcTU411LUgEtAJGIaYAwRkJHR8CjdY7Hjj7gXEPXBtZryYMHDyAKjNtiQy6qjJD/yfvlf+olY5bfKyFyrZpySqYREoVES4kEkhRIIlGIXNQkgRCeEBUxgWdD052zc8+IEQpzhLQV1/2n3Cv/OVNbwuARAqyq+fLqMVNtKPUx1zc/5YubT3lw9g1OZ7/OdHJM8+Qn6Ml9JlVBkpqkcoxyHtRIms4htcL73ICeliWDd9hgD9Ng5xxWG9IYVnB9e8ODe/cPB/GYBCEpjMxnszAE1JHh6vwZutcE1+Vz5zDQrG752nd+E68izWdfMDQdvWgIOPQXhun0lJQyg85ManwMzJbHhKbBTqds+y1+e8vm9gX33vomn/z1n3Fy/IDBR+aLJUnlSGcpJfroTi645pOciqNz4yKO1ucgQaVEELn4y5bhPHQxpaJICWvfohtaVruOk5vvUkzeYtY+Z9Vd0/WXNN2W9focIbKyBzVaBULYdzR+6UHbf87Vti1ffPEltiqZTedMZnO2m4armyvaLsOqS1sRZcSOqob1en04/6mxyTUMw5jS9XKIuOcGvWqvSimnMonxea5Ky3K5ZDKZsNltv3Ku+rv4F5tdi1DZYi6kZte0bEXN8d0zjh++j67ntLIkjPwOKQWzSUWMBeadCrdr+ItPPubevXt859e/zV9/9FP+zf/+rymk5l/98IfIOltrHj9/BjqOkFxGO3qCFPLr6vN5LEpB7wZ8DNQqN5VkgsG7rOqQCaezIt1JgYyBSVVnoGvMroRvfOvb3H/wNrPpEdsuYxoWp3fxIjNKpFSU9XS022iU1jmNV+XCW9s83BEiM+6kUxQx0G1u0G7H9cVz/vQP/oAUIt/59ve4d/8hu7anc0NWXmiFLEYLjhhpmDHSDf1rBb7vL5kixKxGTtGRRLZNRUCLHAoiU4SQiMIjpBkTlBRIgy3mXN9u2GwHWj8gYsAmT11PePeNM4wSbLYZV7De3BCjpyo0p3ePSUJk9g6ZpSaEBCXzgK4sqe0EpzaEqiB2CSOL3CwZmThSFgdg98uBuhrVRImqqoEeZL7fg3PZeSLyWq+URglPStmeHHwPSaBVgdF53dm/T9bUWAYCjlIZlJRokSHvaoRp25AI5K9nSA6XsiVKI5BKH4bKRVEwJEc9rynnNWVd0e3BzojR3aIhRiQaMQ6qb1crfFC0EQafOD69S5CW9dVFtlwKwdYFmpsVq/WGWkWK0nLv7n3qScF0UVPXJeLsLGNitmu6wbFurlmW2Va1ZwjJX+LQ+Es1cZxznJ9fcud4kWOmY16Q9zabmAKTyYSTsqYqJyASXdeQUmC9yUlT3nvOr9bMj5Y8uHd33DgCQ98ifM12tebFs+ekEDlZHvP8/AVD1yNkomsHrq9uaduW3/3d32V5fMy///3/izA4fvO3vs8nH3/KbtcyrWqOjo559NnnTKYV9+/fJaXEo0ePDoX0/Qd3MSOYLIQwQhknHB3NM5C2rhhS4OnFCwiRo/mSQtssKRzVCrumQYsMtbVKY8qK3W1W5GT7zClJCFwIpJChnUZpohI471HWIEdJ4uNnT/nG/BsH2PJ7773Hj378V1S24Le+/1s8e/p4TC9QPHvymKbZ8cMf/pDj0yX/x7/9t/gwUNUzdrtdjgysC0BxcXExTsJzd36xmLFcHNG2Bbvdjo8//pjPP/+cd999lw8++IC+77kdKd2FMSijsWP85B72vI+WM+Zlp9La15t68Y+5/v4pyT/i4DBOrF6dJCJyp1ZFQ1VOkEnSFR1CwOA69tOUEAKmLKjGBos0kkk1o2k6ltMFs3rC5cU1RheocdEoRh96ZQxxcLRugKTQRo6y7UBKjhAhjdDszH+R9H1LXZdIcqMS6dFaUpQTisLmgwRqbETpA7RQG5sTEfxwAGFut9sDQO36Ok9t9o2aJAQTa7OkVmkKo7BFgTYgxuaW0AIlQAkF6hWveHK5O64UIiliVKOv20FMFGVJ227Y3jY0bQaea2k4Xp5S1BWr1QopSo7mS05O1jjX03Qtv/d7v0fTNHzwra9zfHKHvnfMZpPXm05FoPNfUEzu0qx3XF/9DZNFjbx23FxeMcQVswcTprf36dMx25vHVG8e0zZrTLjDcb3kp1/8GW8+rCn0hM2qBXPMdDJFiDVJGXwUiDQgk8DU8Oade4iYGNoCOymwVY2rG5qw43J3zvEbb3N9+YJqEfAxcHVxQzf0pGGgiZKTeQ1JYqzm5N4xm1XNt9/+ba4uLvns6ef84vmHfP9b7+Lrkq9/7XdJYcG95Ts08pbr23NcTOBDZibJHZVcMGBIxhGNoIs9okhYLYkRbDVBWE2bWlbrCxS5eTirajoSs7M71MnjQseiNgy9I3hHO2xZlEumpkT4SOcayrrgvLvkdP4QbWfEANImpEpcXV9RTyui6Hi8+oRtOEKkgAs9R5MlUVXYzwNGR85Xl3B0D+cFy5M7xG1LJxPCRHb9lrKeUM8reneLsZLlpKBZRSbeEAbBYjIlJMusmoFLXN+u2EbP409+galqpFZ505av196XC8iGq6sGY6CuIfTwyS8+R6Qp89mC6DxiagBHcD3DYAl9j+want18zPvffh8tJxRqgghbhuQwtUUqmBYlUsLQJ1K0PHt6iVFzEJGnTy64f/8uzjk264GmXVGUCeIpfT+w3q4wWiHSBKEVfS9wzQ5ryIOdKBhcQqoiq7eGAd/7DG2sKpSwxCHgnCEJw2p9ldfaquJq9RFD2zKf3WW1Gdi2DUOC4K+pqgkCgzWawUeUPMbFjhfPbyGuKWyFsVPi0GSLcNthbYE1NVJEupXLqRrJoAg4P+D8gGSGMcUYEjAckouUMtxc3WC1wWqBkiB9T9IKQWYLSJEQUpFCQqus1uxdl7kxHspSEXTJ4BqCb/I+YQ1dG3CDB5GnkcYCZLi0SMPo948gPCFA2OwQCnwM9MG/1udRCpll9+QGgSIrslUU2X6MgBQ4ZHYkRQw7pEyk5Ekpx4QjDD4NDMOaUhW4tCPFBtc6tE1cXP8RTy7/km+89T+yffYpm91zqqKkb1ZYvWBZnPJe/R7TWLIsSxSeoZgyyILF9Jht24DPXIXSTg6FifcD3oV8z2RWJO+HVfviHUAbnWNqQ2C1WWOUPqQ8zmaTA++o9z1SSWZHS6Ib8Mogdje0o0r701/8mHp2yuXTLzl9622kLknbDnE0zcosrRAu0Hctvl/x+NO/Ybq4w/rqMcvTN7BFiTQVF5/8mNnyDkkEDJEYPX27w9oSXRZEYalnNVJohFIHJqDQ2RqxvwT7FMmEMCmf86McVbuK3gms73HPfs6mf8rF7SNEVbMLHW1zm88aWmUFmR8VOeNknH88u/P/l8t5z/LeHQSSzW7L5188xxhJUU+4e+cMa0tsWbCLOdhktV6PLA6FUhy4gzGEbAcXOURhD9YdhmGsJfLve+/Z4wSFj8xmc05OTnjvvff46c/+ZryX8VCwSeJ47/Ovuy4w+IiPAhUTboiE2ZS37r+LqJcEXdB0Dm30qG6KqFHclIDNZoOUkjunp6zXt/zB7/+f/MrX3uc7v/otjsyEn3/+Uf7zo1M2uy3PuDwoyWxpUSo/21JLgvOHukYKQety4g6M+83giIS8HimFSrmJs1pvOaqnzJeKyfyId97/gDt3HqILi6wCurTIeoKdztHWIIwFIYhCErQGozPHRu45LLnFIZOg0Jq4u8IPHe3TL7l58oTri0vObMni5JT5YsrnF88opzNOTu5himIEy2uSyO+qtgYtNJVRSPWa7VRCEIQF6SFJpIhIMhR97HvjYlb7eW3Gs7oGNJU+oqokm/XA+YsLCgvzUlBOCko7Y744JRloPPz8y1surq6ZlpazkxmbXc8blOyaHVMLQUh8lBmInwQxaIYomSxOaJoGt7rJg5cRmD+ErPrXKVt85RhqoPbKLKWJQhADWCMRwRGA2lrsKN6IMeJJ2NKSZFY/msKgjRqHGIlC5Tok+pgVwCmQpECLrFBCREKMuPEdUjrzQhGSMGTTstEKqTRainwOjAFpJJYSLQoe3n8HdEFKEikcSkVU8mid1UFRKShK+lhwvk4kabh4foHSJaenZ4QhUBaz7E5wkoIRtlwWWBTBRb58tAG5zhaqWrCYLqjqkundgpTyniJiVjnf3q4BKMuSgw/1H7h+qSbOwXqkdVbQdAMxjF3p8eUvJyVKZr6CFDInSSmF87mjtl6v6YJm8IGTkyW1tZTGcnR0xM32lmbTYMficDfKjnwY6LoOo7P/bVrnFKZt0yATLI6PIQnW6+1BNdJscqe7qvZRZy3r9Q4p8+K63TSQ5KiqsCMfxGBMcZA0DWPsI0Azek31IVLbMgwrooj4BIPrMUU5dknz5u7VqOAwWTbohsCubUhJEESk0gqtLFoHts2OYRjoe4tzjvl8ztD2lCanXpVlSd827HY7TKFp2i0XFxc8eHif4+NjXNczOEdVlvRtByOD58X5BX3fM51NmM/nGVa8247NtZRTf1LixYsXfPDBB5yd3slWnM0GFzz1dIJRmt4NhybOfuNRSh1gft7F1z7he/V6ldq/v/72f78qB/67VDh7B1Bi9F5+5c+zTFQlSFLmCedIevdDf0i+iDF/fi0UYvTY7uWD+VkrD5YlKSXKFgihDg0xGSNRJHTyaG3Q2pBShKSwtgRAorItRspDQy03WSKm0Aefa/53FUple0Pa7wyAc4GiyH/edfEgGW6a5uB53ieXFUUGmqvx3dc6T0ik3tumFEK8TBmRUmRf73hfpUg5mWScfCSpSXE4TCtTyNJ0ZRTlpCYRUNIQQ35fCmMp64p68AgVWcynvDi/ZOh6nm2e8Cd/+sf0ruMHv/3b2XbhyPHwr+sSkMod2+6c9eaKVtzgUsPZcsHqc8+62/Lm/bco7Y6uf05ZGHQskQhePLni7GjGr3/r2ygBVlcslhqweKcoZgXKlCij0S770EUUVNqSXESxJKSA1IrptEZJsFXBrtuNSUMtu22LQqGTYDIpuW0UwQuMMhhj2ey2VPoEIxVh2JHCitmRJwrJfHGMLAtWNxumc8Nuq4hCIpVCao2QBWn82ZemRKEQKkMGjUxoLWkGTxt7UnCEInMcQgwgBpwX+aAbMjsEFRCCnE4WPSl5JnpG9AkDBAJD6jEj0JsoEElAiAgZAE/TbWnDCqd7tv2K6B1GS3ahASM5rWZMJzO6PqJKi2kjQmS1nKDnollRWYtMhiRHyJ81bIeWelJS9BK0RZcly+MlRltIifl8zkXznFk55c69B2AUn51/jvevlxW2v3+7Zs+uEkQ3ILEUtiCGQLPbocsaqQEpUcYSvWNWFBAhDp6iPkJT4ZuGzjZU0wlKQYgdTStyhLWps8pEKJqmxxh1sN22TW4iOOcZujY3i1OH23lElNSzkj50TBXZOtpm0G2IaoyWduN6KiBKhq7HCIkMAqstMcm8D/eC2CeU6Ckq0DqrnmOAwQ8Upcb5dkxH0lg9Q9mCFFrwHc73uNSj1QSlCki7rAxJiSG06CSJMSCFZrvtsNrnIi6C97mwSThSiln6LQRKiVx0ubzOCa0IrkPaConPB9KUMs8uJZARbSTd4PNzHwIhWoTOzYMUI2lcuw9pTiID5IUMBJ8bIXt4cI5p7VCjKtJ7TxARabMU/fU+jzkUQIwT0T0Mcz9lTaM9KCWR7fgix+nurb4wDgbUhMIs0aqiNOJg+S30EbebR5wdf4vHl3/A2fTXWG++YFJ/l1L1lFJyFgRLk6OrpC2JQoKZUFQlUb4EEXsPxih2bYNzLjc6YqTvXbYhipxaJESA/fclcvNHjwf4EALB+UMy1d4WEzeAqGcAACAASURBVEI4FPtReIRWzGdTdhPLlMj14y/p+w3JSCYnd+i8o9veQhIs7DFtaBlcIDiXncm6xOiCFHrqYkLqW4SCN+6/z9PP/orClvjo0dYitaEci/KIpLA2D4JMPvTocS9PUh6YjXsbY06aTAcLdYyM9uWEFgVFvQRp0OsNJ+YO53TEvqEwGo+EMTFGCJkbcyEzSfYspNd1SSGZzmbc3q7GJE7JyckJx6d3OTo6PgzTvrx4ztB29F2H9xGlUrZbvLKMK/XVd+jlc/wyDSc3DMTIxInj31OHgd3+PRb7rgtffTMzBzpbfKOQmdk5OcLWS4aYVW3WlOPHvXyniHngutlsKIqCZ48+5unTx0ghePftdzLTtOmZTebZ0jLkABT5ypltfx7efy9e5K9n/7W7fvhKGmlIEZ8SjJb4vE4nYgjYqmQxXzKZLagmU0KSGUtR1ZiqJgpNUpooVb4NIq8ZQslDAyevGfkLMiInnEY3MGyuaLY7tlfnbK+u0H7g7skp5XRG3/fU8xnlfI56ZeAcSZkrM9rPImRVyn/+I/ZLXuP3lvL+oYiIlK3qxJcNpUjCiYSRhiQ0CE1ZabQVrNc7lEhMqgmTmWRaV+MZHFoHXZdoeo930MgIwjDEnn6AQml0jCSl8s86idGBIPAxooxBGp1DB8bmi4gCETg85yllyHSOQxcksuoeaUipQwtJVApDvv/7/RD2iApJVHndJMRDyAmAHtV/khHmrDInZs+O2VsAe5ddQEiJFgmiwliRUxFlvp8+JYSUme2ls9rHA4vlcR4ui1xPEVOG68tE0iBEJEqLD4mhZwyLaSmLzA313iOSGK2FOSVXSg1BkoTJZJvUZQTN4LIy06+oO8V0VmFs7j8kNNIXlD4PN/sQ/9H2vl9qFZVKcvfuHaqyxruOnXf0XYs0lsV8NkoMM8MjeMX9e2dURUnTtUSfM+5dP3C9eY6PgW57n+X9+1RVxdDsOH92zjB0TOqa65sVfdfw4N596rJCJNhut8QY+fo3vs75+TmPHj/hwYMHHB8f8/jxY5zrmU7nnJ6e8uTLxzx9+jTDd29X+WsKOWo5J0JtaNuWtm05OV1S2Iq6yNGTAkW/HQ5JBuvbFa7vR/mZPqThKBRDCJlPYgaub9esu5bkPCJB3w8Mg8eYkUEiIsrmzXLoA67rqRclk8kR4faGm5srjCkoiglS5qLCGMOjR4/4/ve/z6cff8TTx08obEXTNPz1Tz/k8uqcH/zgB/z4xz+m2zUImxtIu3VDZwasNtxe3zD0HTJJZpP6EEkeQmC5XCKEoCgKLi8vUUpx9+5d3nz4NqvtNc+fPgPAKM16vT4oM/bd1MrazEoJw2uVxv5911ctUekrv/8VVQ3kyMzxQxIQ/1ZqRyLLqg9+/ZiISSBFtgRprZlPpjxdrfGjpxGZKK0mCFBaEkU8gL1ETJAkg4+0o/9amzF2WVuESKiUcIC1grKoMVoyuDA2FsUoVcwLRd95pNBMJgti9IQQmS6OIEA35OjtEAJKmTHmM3NLUhIMgwck3gWurq4OaWzDMGBtjrwtxsnFoemlwGpD0B6p06jYAakCauQ7qZTyJCVFVBIIEQ/WtECCMPrLQ0CiGIYGFzzdbktRFEwmFUPbsVzMCSny/PlzVn7FfH5EPZ2ATLR9Q9OuKWvDZGr5j7//+/y/f/7/8POf/4w7d+6wXC5ptrt/mgfs77iUKimc4DZdYc4s+uIeobth+t1Trr78mFl9RN9p3v2V9xDyglI7hpWi1pqbDweu0+f84H/4Dj5d0+8GpkdTVKnQWlBJyeX2impa0VyvKI2l11vS6gipLE3YcDpborRkdmRB1ajihA8/+TnrdsdnzzvunlVYlaAdmNYDs6P7+Mslv/HBb/CXP/pDynLgg9P/FulKSAPSbVi4gYmeUpUTvnz+H1DdCe0G2p3mzfmvsl5/zq58gReBIkWUiYhYoCc1XbtDJZjPT+h7ByrR6MxBij7gQ5Njp41hEB1Nt6UsLG0MXMZIbB4xDBHpDEJHIoJeR0QJL64vqYwhCBhSA6InoUiyouwjpxPL//b0z/jOUNJMFHWIlLqgVhZrE5jEG9NTVvSYWYXqd5jTKTyOFPWUlQ/EdU+87ggk2v6W4+mE85vHPHa33D+9z7ff/A7DrSOExG6TmM4K4qTgxSB5//SIyYPf4N98+SecXjuWyzOMffTankXIzKvb1Qv6bo3CcHL8kHJ2zNMn56x2W4wpMGpKCiXO1SxO79AOLbOzO1gKyijwybAbOmTs0bOKqAd6H7l8scWIGSF0lMWcoix5/OUL3n77bZS0TCclsU+EztOngDFH9D1s19ekKFA6sRtGxtxqi06OWwTzeYnznoREUuCTow+emCCERB97JoVhiAGPhpBQItEHidQV0UfatmATBlZFw9mJJMSBttthVcQrBUmR3MB8dkyyCivyXi5CbmLv2g0+DAQig4/IGKlNBTphlMKnHqU29H4gBEG2+Df4ATx95jTobFlxvsspMy4ifCJFh9IDLhiCl2w7h1KZ6WJdIoodMSaszUORwd0QcVhpKZSgjYoQBQjJZGZHGKIdG0iBgCemHk0CI1BGEqKnSDWuhqbrSFIwradIsX2NT6PA6HpsdoxDhFHRysgpiEiEdKPafbRwpJib01KOw5VE36/ZyU9xvkGR95uF+Qa75nM0DbFvOS7vsN38DRNZMneR+0EzL+ZI0VIvvkssjzHVnHa7QU+OSUrhBk9Zlmyb3WitcFmtpTTNrhuZh1t0kyPih2FARoPUEjWqFIDDOerq6orpdMpqtWKxWADZgt80zRiKIeiblrI03K6uGbYNyhiG4EldIGzWeOWZzeZcXX2JEZLd9XOOHn4N7zpkPaO7eU598oCjZcfQ7bB1cYhrv719xp2H72f7iZ7kWOUoUIVBxITQLwtaPwwUpclKh1eKo70CBV4OvdQrSgURIKmc8lJPDN//F/8L588/4vOP/z2Xj/4dx0XFrQqokAgHtsWeOyFISWX7xmssnYUUVFVN0/TcuzdFScPR0THf/Oa3WB6dsFgsmc1m/PmP/oIff/ghT58+BSDEDNYIKVJYk9ceJZnMZwclf0ppREpkW9P+npnRZre/j/twjD1D8zBcHI+eSipC9JB7t/iUCELTD57JZIY4uku0k5w8KwTaQgiZixVjHpogBdH3+G7Hl599wk9/9Bfcrm/59q9+k+9973voJLh5fs7bb77DrmnYPPqcocuW+uA8Skm8H0hJjQrDPMyDrJZMIYNaM+pQZpskZCC3sfhhoO8G6qJkUk34+gff5Jtf/wZnZ2estx3b2rGYTDk6ewBa0okIpSUc+D/53Y4yEgiIJLLlUkpMiqS+AR+4Pr/g9snPcH3P9vKGQgrsdEY1nRK1QZQ15ckJekwYjiRSDOgokFqhhMLtw0qUeq18pnwlkAG571whiSohoiQlic+aRUSSWGMhCu7eOcFYwfVNz8VVS2Lgnfffoazyu1laQfSJLsDjx9ds2wFjCqZHc26vLuldwOiCx8/P+eC9e8RubICMtU9kz6KBybRkNlvQ3lxTmTyY82RGXYiRNKanJcb6SmZ1ZUIShv4w8I8xZtbZOGSIad/IkWgjCSmrq/xYKCjxks0Vw0AUIIzCjgyyPYtsX5eNaOPx17nZqKUikPAjrxcBdmQhSaUpJhPaYeCNB2+OtXz+PqQEhclrYZJ4YSiUZrVeEVLi2Yun9O2Otx6+QV2X+CHbJaUSaKWwZYlReZAd5MiJipnnE5MgJk3XB9q+5Xq9OaS8LWYlRWk4O7tDAtq2R2nzj3qKfqkmjuClfFRKiSkLcpZ7Rxz5GW7IAFcpJUoaBu9GD2meSmmtkYMjhXhY/ILzY/yjwBaa6+trrq+vqcts4yiLmqqq2Gw2VKXl5OSER48esd2uuXN6j5QEt7e3B7/qbDbhyy+/ZLfbcXR0RNflVCoRE0kElCrpm/yAGduyWVtcGbBaUk4sxtZ4NyBGkGprmuyfVCY/hM4jE1mF0nTs8+x3bcN2syW6SFnkH0BKgX6khe9hY1pb+rDOX5MQnJxka9d223B6Ohb9/cDZ2RlN03BxccH3vvc9Tk/uHOLSU0ps1ju8f8z777/Pcrnk2S4rdY4WMwbXsVlvmNbzXNS2O9omK5uWyyUAFxcXo/w7NxH2DBxE5OTkhGk9wdqSrmtoXHt4cZR6OW1vRnbK4vj4v4gmzt93/V3qG/m3xBpfUfOkTDJ/1d+c9l7hVz5eCEHv3eH3JSMrKviDVc7a7Jf2yROTx7k0LnAcfJBGmvznjPGlQiJkAqGyskXl6UWeTCgkEjkujLrQuC7lyFRREnTeqEiSGLLSQescdVnoYpSkZj/p0Gc/c/BActjCjNT6rBqSevSGSoka7WVG5b+fZJ4Cy7RfVHO3O6Z4iBZP44RViNwEC6OHNsUI0RNSHBMgOMC9pcyQRfzLyXPukCu0tmOaVlb4FEVBURqazZaffPgj7ty5w927d2lHWOXruJSUmEIyJM/zp8/54de+yycX+f2+ur2Bfsd3vvY+zc0qJ8OJrGLRxnJ6Nufq8hprS27OG47sFKW7XEwmgxAKETXeJYoi86jadkdhKpp+IESHcy3SKHovENqxabaH1ICTE4s2iWbV8eDsa2i54dq1nJ2dMp3WLBYLut2KZnbOGw/eo94IVo3g/slvEKMjCsfQaVYXT5gUJ1TFMVJqqnLOxl3SDi3zeY1QEq0MXioCPT4E+sETfMqx80XJEAY2/S2d69Eyjpt/TjPYS8l9DAxekJIYv4eCIbSIEHA+MrgdRpoM6ibbVJRURFuw2V6y8ptsh9UFIkFIfkze01hZEFNEa4GaTokpQ9w3uy1Lq1gPO9CCoqiobEG3a3jx9Al6eYbSI4AuRp589gUfvPNNtl1PcI7Br+j7xHxZMtxu8NOSX+WIcJywk9dr7QOIITAMHmsqjCrRqqTruhzPKjKos6wN0XcIoVAi4ocGP0gGt8O5SNQVsmsJySPxSKWRI3g/+fxODsMwFi9DPnQoxWazJZRjDHOIGKtJPo6sESi0Rcoshw9hIPoBpDpMsRMROS7MWYWblVtSSlIUBxBu5pakkR2mCEmRhCSKzG3oh5yWqIR8WWjFbO1rdUvSmX/iXE+M+TziU8L7gSjGRkJMeJETPBKjtDuSVaeksTntCXEgEvMEcryEEMSwD0+IWUMzxkdLYUYbVFb/Jtd/ZZL+t4cNr/4679UGIQI+KBS5US6lRKYMLReRQzEeo39l33v5uV/Xtbeb7C0i+4AAkRj32FG9cOAq5OZNrmvGwYMwo5JH0Q/bMUUl/z3nd6z7zzGiRlEjwxZb1BRRYrafMz/9F4jhBaU6hcVDGG7w3mPrCYMSpCHvtyHk/5c624+zilUjJflsOips9udf7/3BXpNtVx4tJC7lX282Gxb1lO12y2Qy+crPUCtLSomrq6vcoJKR9e0Vxkrq6YS22zEpDLv1hrKskSkSXJfvx/jvSWlQWjAMPYUuMULSCY+paoTIk3JdFUhVoG2dmwVaEFyksDZP9o1FyqwUEq88F3uVyCGBUuvD+7lv8ESRbQ5CCEQEXUtmi1OevvhryvIEZySIAZVcLhCVIvqXw6gY40HF8fouQdN3OQUo5NjkGCMnx3f4+te/kfl7g+PBvfs8efIEkaAsc5pX23aHn7s1OXhhP5zbF5xCvHxnD3zA8b5K+bKJs0cS7M+WOb4YUpdIJMSoaIivFKoRidKWqApiyvtjXivy52a0tjCuS5pA12754vNP2W7WiARnZ2dAVmGXxQRPQzioq+JLWLcCMSqz9ivH/pkIzufvWeRznuSlusiTDowU7xMUMJ8vODu7y2JxhLUFQ+/onGeGRJicOBXHG/SqrUwwpi/JvXom5H0nBNrVCtd23FxeELqG6PPa4caGmCgqdFGgpjNEYRGHz/tS8UTMhb0kM4BewrZf3yUAHcmw8f2VJEkoQp7W5n1XahAly2VJEoLbTcvt7Q19P1CWJVWtKQx0Q8A5QecS28ax6TqCg6Tz+T9JRTc4dGnoB58TEWV+1pDq0L0RIjsKjC1zeA2CkF4OwaXKiqmgZOadxVdisff0BCkOjgPvPb0bDgpMpVSuTciN0Uge8MpRTWOMwchsKRMxEaQkDg6U+IqCbf9v/m0OVQaAA0IeHA45cEWgjMKP6ZBJCKppDmTINqysFhXkdymOqlAXwwE07LuOFAfqSYnV+UwiUSMvNY6Df40YG5BGKmRZ5PpFC6IIORg+KmLI71LX53OIrSx9AGs1ZWEPz/4/dP1yekaRuThNu8kFn9KIoiAMht3Ixum6ntPlMcJabm9vDxufUoq+TznLfiIpy+wBv7m+RMSEMYr5LDNdNptnYyJNjZR5iqGU4uzsjJOTE4RMXF5eYqyhnhRsNhuazZairihtiVGWi4uLDJ2zJdfXlzRNM9rAoG96tm22XilysdAPLf3Q0vQdVVUcmhV6H1PrXf7+Nm0Ge9XZwuJj5NEXT2ibHav1Bj/aqVJKFNZizF5O/PKQFmPkeDLjWdNzeXlJWda4ELi53vDwoSf5HCn9/ntf54svvuCzTz/lk198woMH93hxec6TL79EWUWhC0iSn/zkJ3z/+9/HKs2HP/oxR9M5R4tjtpuO9WaTOUWnx6Ndy3H37hHT6RQpJbvd7rCI7w8on3zyCZ8/+ow7JyecnZ0hxCkXLy4Onl8p5ZguMW5Wo3LjvzQmzqvqm3/wY4kvF3ghDnJhIeRB4p279ZEQ9skDgdV2g+v6PJpSGVZdVRXS90QRKQo7MokEQazQxjB0jqbpMCY/ZwmJLg2xjwRvUNpkCLrU+JEar9Hj9FIiRY60tdbkJkuSSF2ghEAog1WG0siXmxNgCoUyBUZlG4IQgqvLW4ZhoCwqenIhYa09SMC99+O7pzFJIEZavS1HW5fMjSZF9ozne5VQKQtzQ3KEsREjUySEnuBz0zYmnwstl+0Ek1lNCuBcT1FU+V7HxGy2IKTEtK5xQ0RJw/tvv8Vmc0sIHqMU9+/eZb1a8fTLx1ydX/D06ZPcjHxNlxWCx7ePOHvrmH/5z3+TN+OUVVxwtfmSuw/eIPaaGCRdv2UyVZTVAjdoEIrn2+d889d+HUHBO3ffY/X0kounn1PeOUUWUAbJxCxYb26pJjPm84qb62fMziouH13y4MG7TKYVMQ6U85pHL855evGC3W6HGxx3H9wl+Y4HD9/kB7/2X/NXf/lHMHmOxfPhT/4cawz3j76JKp/w2Rc/5XL3M9Ad777xP/PZi/+Vk+WS4eaMi82PeOP+d/PErhtYTN+l71sQN5jCUk2OENLw7PaCptkQe0cpSgpZcOfomF3fIckKNSc6fPSIILPNSznKoj5YdHdtj9Ul1hRMqxmrbkVhSsqigpnkzuIEFcFv25wIFDtuXKBIHc+bG47KgiQN/fkadTqnLCw6TLCipt3sOCpmXNDjY4bf7ZqOYqbZXW/pd45CWoa04+LmGf/qv/ohn//sU+x0yvX1Cx6+/YDyaeD86RPsyRKnNJvLz7i8esHd5X1+8N/99/zVl4+4J2dcmYAwJUq+Xq99iAGjSypbEb1ktwkI5fPwoN8Qwo4Qe1LcUpUzhrakb68wqgMVGQIURjMMnr4fUDKwWuf9c75YEH1HSpKhd5AExlS8OL/MDcG2zcpCWzNLmkJotA6sJPQusF0DqsiTqegJwwAiNymDFyAjqKwqbNsW53qUFlir8S43RkIIhJgZZILMTvEYvJAIXWBkwPc9EkEtcsrGXsUIkX7Y4brczIrJEeKAD56u9YTgRjaPBQRxaF9RJIJ3gr4PGLsvzDwhbAlOo6wCpUgx5CI35IIkpDAqIQPKZEWAtTbbvpQijHaKVw+mSuVD595mpFVeE62p0dogRXhpaRj5dUGA0YIUArZQaAG+b3MkecoRsDG9hFC+jksIgdSjQmlMHssH8awETeOUNiaBJJFCLgBCSDnQjEAMAa2zFS6EXOTiBcZYrra/QIsSicGqRJlg0g9M5YK3Fm8g2wsiJeLNf0ZoNuiThySyPchGgZN5H9ufbRQ5rtiavL/HJJnPj7i6ukLXFhfGgloqYgA3OKaTKiu4jSb1Paao2G1W9CFycfGC2exr9G44nI9i8mAUxWTK9uoZSSZ0Gojd/syR0z+TkuhSY03J0Lf022tUOUemhJ5OCW5gUh/lRiYwm59kNdB8whCGw4AoKsWsnuWfh8wquJkscNGRhMKWxSvPXsD70bayt3iPBXYiD1r2uIEkBVbkqOJJUZOWAx/8s/+JX3z8r+n756TOHdJ/VNw3vICUh1QGw6tDsX/qy3vPze2aqqiz1UgXgGA6mfBb3//NA/j4jdMzrt96h79YLGj8wJAC0uRzfeg8y5Mly8UR2z4r+bXWB17MvqCUUuYh71igDy4cLOvb7Zbdbkff5zAJUxZ0PubaAo9SBs+YZioUrQtEWu6UFXIyzXYgIik4ZJSgsrpJCEE/PscFHTo0nD/5gpPFnHsP3+Ddd98loogpcnJ2CusXKGsxZXlQUldFPlNaa+mHNgPqh/HMr7IKRySwWkNMBwxFSonOuxEGK6jLgvfefpe333yHr7/3AZWtyLu9oZxOqRZzRFkgRGJaF/kpSFmJrkemkEpxtJU6hqEjhUi/WSGcw7Udsm/wUeJDYNCa47t3mc6O0PUcZQvs/Jioc0NEvdJgT+GlOjymCCmncb12HIQALSAgCCmShCKlXCvossoN+3JCaTRVlXj2tGPXXCBEYtPcAPDmvfsMLrFeB7o22+gGtnR9wEWJKkrcACFITDnhxcU14uyEeT3lehc4KsT43AYiCqMiAeh7x1FdMlseo02V1TMyNw+RAo1CSYmICeeymhVGflbMSq19A1hrDTKnKGf39pjAFB0pgAlZICJ0fgbFmNzrvc9DG5nT8/ZNpv3Pad94fNXKuF+vCmOydZd98zz/e1ZYhJYMMXJ8fIYtCxDkwY1SWSiqcpM8CYGyBRfXK3adwzkPIjKfFkwmNUpDrUtcPwASH0O2bkmQwubYeJHyfRJyfP8FnevQQmBUQYyCGAa6bU9cdzw/zxbI+dGSYfgnsFPlIjbgU6TftbTNCqOy3zWm3KlKwbParZGdHePFysMDW1UnWU5oytFS1NLtJNqWFGOctzGG05Mjbm4yZPXm5ubAhDk7O2M6nfJH//GPmU1r7t57yG6zZbveMJlOWS6XPHjwkI8++oQU4GR5Oqbr9HgfKUuJUoZd16K1zb71Meo4xsh2u+X6+hohBKenx4fNa/8wOue4ubxBa80bb7zJ9eUNoRtot2OUqQugs11rD3/dS7+8H17pzkd6H1EqT33dCADzg+P8xRXbXctkjJ8UZN/rz372U+4/vM+7777HRz//OVWqePP9tzHGcHl5yeXFNQ8evsWLZ+ecX17le3HvAZ9+9hlXV1e8Wb/J0fGSFDyPHj1Ca81sNstdXrK8s213L79GIjerWyKZmzObzbBljlXfy0OttZycHCO15ubq6peKRfunvPZd2Vc7tq82mP72pHN/iVGKB1kbkAiI5FHCEEK2GSU4wA37PvD8y2d0fUtIgWJqxxhE0NISosvTGiI6GKpyhi0rQB8aFd6BrUrYJZwYMKY8TCRCCMSQUFJnHo2yCKEIIfuDdZUnegKFNXmhNKYArTEiHTbXtu0z+Fi/7ErnTrlCSktRVDl9YGzk7f9XaYs0elTGKAozpnIYne8NOZUqJUEUOX5TiQRS5UnrKxOG5HNRk1JCqogSBi2gT+IAkez6Hd3QZ+ZPEpiy4rgoGYas2JkYjYuG1t8ym824vr5mGLK6r6rrHIvpPZvr29eqfgjBU9+7w5GtWEjLn/70r+nUjvLNWQamT2eE0HD33ikproGaPsAQIr/z3/wOs/qEi+45H37057x77w3ee+N91N07tKFh119xd77Adz2721vqYsJieYy7DQx9oG019fSYcqoJas2q20ApmDJFo/jO+7/Nj3/yJ0QT+fDj/5vybkc675GTjhgG7hzf5+HsV3ix+pDp7IKZuoc2BRRr2iZw/nzNu/cK4kXP33zxHzg9uoeRiuPFPZrtgiA39GGN0g8gRXar52y3K2IfcNM5pdU4BsLQkBQ0oUEaneNBQ4MWirIsWU4X9MZxpVq6/hati3HKqJgVdwjRIEJNoRcYdcz8aMJF8wkXt88IQiJmC67DwDAVVC5yOayZlAYtybp0ZVGyxJaCbtewCjmmfK6KDDm+vWSIjkVVo1Jg3WyYn8z46JOPeOPhG/zi4hPuHR8DkeLsGO8Ft7eXnL7zLub5wNtn30Damr/84gnlF1/w47cTb/U1PhSk16xQFEJwdHTMbr2j2e6oyoRCjHZkDyIHDdQTTZKB9e6W6WKGMYogNVVpcEMiBI9SUJcL/HZDDFAWM1Lh2W4bhDQ4F6jqGd0wsNl1TOspPuTDZPSG9eUmT/GEHiX/A0IaYor4YUBEqOqCGDRKZc+6891humptOapayNa5UYadxRsBJTOLRgrPRGZQrhQJ4TxSKURRonRWBkmdD3ghDuBG1aTUeKEIKUBIiCiIPkPkBRCTZOgDwYMQClJBYcHYmPl6YSB4hyT74lPIJdZ+eJXl4xnsqJTFSIWQCiMtCH84eArys651Xg+l0sBoY9FibJLnxpf3+WNcnxsDWgYCAkQap9cAWbVGDMSYE720tgc7x+t7GMFaRYoKIXNUa47pliipERJSyryZkBxJCHqfOTgyZMWRVIoYxOF+CJXwcWCqH9L3LVFBFz2dDxwnxQ+W38TICUkY9P3fgRDpuy2TB++jvMSTlaf7SefeGm6MISLROluJQhKIUT21H0R2XUcIgbquM5NLZNjnXplcliVt7+i6DmvKzIqQkt3tGlMW3NysWN9eEoLH2oLZ0SmXzx7l2FuXC0oBOVZZSlIMI6dGEroBITaYakFdzyEkqvkET26K+NBCACk1RqaXKt+qIOFzMwSzrQAAIABJREFU6oupqSS0Q89stjg0ZeAluHePC9irTPbPjHilYA8hQIgMI+/L9Vu2m463H3yTTz/9QyaiRZaRm/YKIyRD8JlXkSSJbM3Z25Re1xUTLBYL3nzzbW6ubul2mfH49MlzCmOYT+Y8+uwRn/z8Iz75/5h7r1/Ltuy87zfTSjucWOnGzs1mN7tJSowwINmgDRAGDOjdevCL4P/JhJ8MvVrQG2HYBgXKlAizwdCXat0c6tatOhVO2GGFmfww5trndJOm2IBc9AIublXf3efsvfZcc47xjS/8x/cZdnuCAe0MTdsKyOBqVgsZgM7sxpTSIRgCbr0+TJFXAPQ3u5959vpemIL3T04wlePp8xeHmiWlBErRtB22qtn3I30f+boy1JXGkGmcBRJaRVIQRnbSihAzMWZunj/hq88/xISB73zzB7z19a8zKctyfUpbLdlfbsE6xrhjtx+YUsQYQ9e06BLnraMhKUW3OBKfjnGUwb1VqJQxlXiSpBDZ7Xf4HNAoaltxdnTCD3/wI9558x26ekkYRpbrFe++8w6nj+5hGsd+7FGVZV2tSDlgEuiQYJogZab+mquba6ZpxPsRReJ42VEBJM9+e83GB6yreeu732Z57yG9D8SqIStHVhobNJXSaJPugKjp0PPNaam/yMD3P9+VCzCSyQg7RSmLq2vWRycoq5g8vLzpuf5wS7OoGIaRKY6c3zujbVsCmYuL5/SbSFMfsR88rrM0XYdVFlSFtrI3Va7hy8+vubze0jSnXG/2rNxSYs6VRSthxdgM+3EP9pTlak23PiL0W5y2AhLlTCZJ4LnOmJRIuYAqOQlr2dUlyEg8c2Mssi2ViEnSrUQ6B9kqdGG6lgfg4MPZ931J+UsSXf5zLJyZPTb32XPvJ+dJPLCEjLPUbYc2Btu2WFPz1te+TojigWYKsxctcrCkLcY4tOt4/uIZgcxmu2e1WrBadjQuS3/nHPudsJBdAT8P78UIWHWz3ws2gESX55jwWAYTxM4/O8apx2pD3baMAS6e3/xM3Prfdf1icqrCRtD6LrtE9IrO1kQrU7Ldvqd2mfW9Y7TW7PcDkx9Yr+UDaqvxU2TqB2LdkPxQJvA1tnJcX1+itCHExJOvvkQrx6OHb9I0DVORJklUX8vTpxsx7l2IcW9VWV4+v6BtJbEiBV+eF433xSwvJqqmPiCQc6KEzrDf7zEo4skJWiO61RBxrj5QIWeTuhg9PiUxedWKavJMaTZ3vTWHG70YMwO0VU1Es99v2e+HwyE5L+DrV5d4P3G0eMhY4sqr2mIU7G42PHjwgLpqJPUIcMbQdjVfffklOUbund9nGPaFMl8ftN7b7RYMtHVDSon9fjiAYyGInC1GQU9RqUSwLxjHkW2/56Rbc3p+hnPuELs46/FBAIRfJBbt/8vrF500zpZp+ef+b1p+GJl4h8Z/u4GMY89mdwM5iX9B1ChTQS6ayCgIbE5yoNdVVQywRDufUiLEQVLSzCh65qxQxWQrRTGKm+MEMRaQpAeHw9paNgdd9OtaPBOcFilN5UrsZchUlUxbqqqwf6wRymLxyplZZ/NNEBmjmJKHnMozrwoIpIkSCCnyJ249/zLidWBQxEPaApKcMgNrovQFayWBoBTCu92OYRhYrY7kXkePq1u0tux2O1IFztTYUpxmIpvNhvPz80OEe9/3JfXh9R3IuUjqUj/xyeOP+fzVC375O2/y8av3eOPkIUu74Pr6Gtuu6EcPacIZaZpXpyuuX9zw5OoJF9sLHplTTtwZz3cDN+MOy8hWXUOOeB8kfaurmcINq6Wlah1TmnCmxC1WjjTuCcETJ83ly8949HDNFKD3I3u9p6otMU1UTpiIrVuzaO8T8yUxHNO2x7zY/iVH63Ny6NgNz1isVyg2JFWRTEXiBB96cuoJKpDiCMFj/IQhknIgxIEpVmzHPWnqSVV5drSRIkBZMBqdLEZZjCqU/yEd5rMaQ1VphhDIBJRzJJUYosa1LUrBlCK60uy2A5OK2JwZsviBKa+JOhFtYiKijCboTHaKmCPe79DOEUMQTbfNpBCxbYdtFK1esTw54oRzxsvPMEbRK5nAg+LTr97jNJ+jTcNyveTz7Vd8mr6iHQ1D5Rj78NoZivqO/FOpjLaenGTqrI30Ta6iaF0U/TiwXHf4GDCmReHQOmKdvFZSmXTZ8zz1wjKFAFh8zLLvoMk5MQwjwcM0jfRTwPtIzoiperZkRkAdCmlrJCRAkvPEJ8P7XQFy5z1jnnB7od0rK9KoWEwVM6jkacp0cKZFp5QoMSTFG8yUBtRjdCtRpGMghFjY9cIuTUmmv1pnNO1hj9RKQxZmkTEyDSdXpfjOh8btrnRIdkY5YXSJcdZKg9aElAX44W7jVxJ80AcZmdaGpBNKMt3I2TADGk4LO1T8CDPJBFCGlMREslJ3WWCpDFpet6RqlnrowxQ2pVJw5yKvKd4KKYNKZUhgStOfNCiDUrGAXgaVFCHupObIAI4jteQ3uzeIWIgj2a3Jocf4PfWDb0IWGZYAHXKGJTIxyb4QQxCjaFdYxjmiEgzTeGAa3x0KCVs0EYv/G0k+j5z1sr5cLc2MT564iwzjnpQivkjoVstT6sUxw/4VRoGtKzHhVIqYEiZbjHaY1oGWNZaDR3ULAX5UQoUAKlNVndzHnKlMJf55SqFzed5MJOtM1pq6FicJVz7rLO+ZgccZqJnrvBgjOucD+1xrYdv2pUbVztLVDVP2vPHGP+bTz/6YtPsMnUTeI+e+rF8ovZV+rfEDsga14uTkhNo1fPjBR2w2Gz755BMunr3g3Xffpes6Saa6vi5AVhRvwzJQXbYLrLXEyR+GXkMxQLa2yHb0HCox9wnxsGZmudG8fpqmIeSE97MFtHxXKHWQbIlsUMzjNaoM0uQZ1xQ5kkqAOdTgr1485+WzZxgte+Sw73HHDXXdUjctaYS8UcSc2fV7NpuN+CLWFbOssS5Dv9HYIn8TBqFBHf6uKBIYa8klYa6qpO84Wq6KJUOFzVJPkjI+RWxKOCvNd4gTOUoITB4GfAG8dpcv2fU7lM4sFi2Vcyzqimm/I8cRo2B9dErVNCyPzpiMJmLJ1kn6qnK4aIrh7nTb5Key14RInNkd/yC9i3yXwSdiViy6JaaqsU2LdQJEXG+23FxvqZojxhJUIPd2TbVqeXlxzeWra2q7IKBouiVJ7wlaQ3Jksgxdy77QNA39MBES9PvMpdtwcromhCwrSct3W7uK/X5LBZzef4PHH/+UuvgtybNfhh3p1rBb6Uz26meGwbN0cAaLTelJBABXmKwZpgKAxkRSwiS+28PNZ6Myt2nBh9/5c6+7BeekpzO2QpuE0raARhptHaujYx7cf4gys+/M7PtV+nZtMa5hsx+IuaRJG8dx17Fadod+SdQ+klY4f2Z5JjO+yKXGKcigGlC2RpUY+TJyEtmcNcScCfO9M+YgTftPXb8QiOOcZXHUkqOnqhx2tSD0IzFmTFJkL8j8crlgvTomE9l7zxR6trsdRmls5UjjiPeRaRrEzLcyPHz4kPsPzxm95+r6GpTj1eWHPL+4whjF+uQcnxQXzy9Ynx6zPj0Wh3kMR92KZlHjnOXli2dsd5ecnZ3RtpnrqyuZyC4W9P3I0AsIo71oEY2u2Fxusc5QtRVhDOiqQiXFfjMw7nalSBVPmK5p0cpy9fKK3kc2uw1GK+qmY2VrNrsSOT1NvHrxnLZ21O0KrWSz/+LL5yJRKQazKUEaPWeLVujr+0vaKrNwLS4nzo/XjLsNfkq891c/4Yc/+BHf+853+fGf/5jrqxfokxMqnbl4+hVffvGY3/3d32Z19Evc3FwyDCNV1bA2in2/kyK8Gum65eHBWq8bIOGc4fRM3Oqvr6/lcDZwdnJCjSyyTZFmvfn2W0Kz9J7tbgcMElP5mifOd31u/rbfPTM87r5+fq1SEska0iz3kcJYXgiKWbOc8SmJN0cQqZu2lqvLC558+RnXV1/Qdlb8a0hotULrBSlrdAXBb6mcR1lHbEW2ZXRi1TSMfiDEgW7hyHmBrSvGvbCctFbgxQ/AuU4aHEwxJLboRgBP46pSyAoQI5tKx9HRGWmMTEx0S0k4ywq6xQprZYqUdS5pbLeo9t37FZTCWY0KsdABRZLhnMFmXbTRBm00lSlFSoiEODIV2r7GE4nkmDEqgsmkMTDFkZxCsXPLxHGQ53yxBKVwti70f03KkWGcuL54jq0sdV3hXENTrwAxVV4tlrRNw9XVFZeXAoS+rivqTOst0/CKgcxiseC+fsTF/nPycYtPhi5VpPEKqzJfXW556603qFctT59/zK5PPNu+5Otf+3WsXzMeTTx9/BWDH1ktIn4a0VVD1zRov0Hblo36lLe/9VucVP+Izy//mP4yk+o9y2ZN2MO+vsR0Ey9fvc/67G2Cz5ycrAmTZl9bor/h3sn3WHYPuOEZwShi7MBcse1f8PTiE37wvf8GQuLxl3/GetWxXp0xTD1DNOj4khRHVGgwzSXKX4DXHNsK1Vb0WbE8OieEwMX1p3Q05BBIOgAdPu+ociUNBjvGtAVT45pM2LfEKaKriXGCZFMpqCeCnnhFQL3c0Kg9VW5JCa6efkHoMm2l2aSBDFTBQd2SzIIwbdm5DHZBtaiohy0xZrZU1HFBXb0E27KvHexGzkwv8p6m4t99/O+JRKp2RR8zMW5ZNSvimEE5XppX7Jo9Xzz+nJYVblTkqeVif4PLShq813hprVEBog+lgJnAJGwFiYpMJDOxbNc469jsbrjZ7WiaBhsGUvQo02LdUWGSjLgEIWR2mz27fcc4OKa5uY0KP/blWZhZhJkpGMbiOVJVNUZnJr8jjrk04o56ucC4mmEcaYzGZIPGoomQAtY5UvKQM67qQGWGaSe+AarCZotVFTpntE7CWDGKIebSXA/gDQpwRmQ9UStignEY2O77whBUVCX9Y/ADzihqbaUcKzKkKYE2DkuLVp5MIPhAmBxYT2MN2t76Y6CKzwquNHaZqBPKZpxVhEGGWa52qJzJOWHIhZ3SggoYp4AAURorlBEfMhTtshKTUe+JPuGDxySDSeKLFKMn1QqjFVXxLnCVfq2NswD9Aq4JweB2kmqMJs84WxADU58DxgjzlMLmTFGjtSJjiwxPaPzjsGdpFhAUv9I84q16zaAzjgqac6rFm/jLT6nf/nXq9rgMTeIdHxPNbOA7s21mRs4sk5lKAEFVAhxmr5hpmlgsWmI2koLTdczeOFprlsslOWeO1msuLy8Zx5Fvvvsuf/kXf4YyFm0cVWXZ7a9ZHq1xVUUME8P+ipzEF8oYh1EVylUyRClMtaQUNmW00fgUqStJx7MlVTIZVaRhcq9jTDRNLb6MOaGxWKsOn31uDGeQYf6sB3PRAjjMYPT8HeqUD8Oo68tX3Fxe8c63fgkVE2+99Y/46vkx48u/IGiDHvekGMTUuQBGKfrXysRZNA16tydeX/Pw7JwP+j0XTx5zefGC//2PfpPf+73f4/TBCaf31qyPWrRS+DHh/YjVhtZZHpwumF69ZOh3tIuWPkamMJKdKZ4shs4ZaqVZpsjVGPH7CY2wkV7eXPH5k8ekJH3SvfMTLi8vsVvwGnwKVNpwFhJf05bWKbZ6IphEvt7gkqQVjZVBKUtOUE8a7UV6VOfM9WbDJx+8z8XFU5qTI55f7dD1nnvtOY3uaNyCfZvprOEqeHavXrJ7+YI2J9ZdzRQnogZXi0xwfPGcBs3xwuFswBSAfUqJlDOjSSStOJoqatPiXMW37n+NE7tikRyPVids0xVVGOkvn4Pd01cNJ2fnVE3L8NIS/UiMAT9sub56yTRNdE3LvfN7LBYL2a+nwNUmMIwK1IrmzQeY0wcCbDY1jTHUJguzBiXSwZJcImbp8o/KBdgxmhSC4OxR9qrXeWVgCJnTs0e0XUMEfFTsx8CzF1cM08gYRoxSDOMrFsuOs/v3qWqFMoppSjy7uKT3Ge1kOBCzfI4UMjGLZFWlCZWhrWuOj4/Z77dcX2+5f37EzW7ENpFFa2To7CdJnU2KMPVoY1msOppuiZ8uBbxwCk8Z4sfElBLRS0rTDMpNPqCzLqEtFlfLntJPPVqL11SImf1uIIcsAJ7m0IKJssWDVpiiFhGOzM9ed/1KZ2mVMHgFnJlT5Iyz2KZFGc2kND/44a/x6K236dYnXF1eiq2KNRht6AOAI6uam80lMYOPGm0Vp2fHVNYV2VhhcNpWfEe1gWKWvdvtGfwg30HWYCy1FjZnCInKiK9pTolxmiSR2Fqsq2+ZRX9PYPEXBHGcULQQGu9UjB23Nze8eHHBfhhxtRgPa2UZo0dnMMpytF6zaDtiTnzx+CkAx8dr+mHD5dUeYwxHR0eEEHj33a/z/PlzPvroE4yF9WrNcrnkvffe49WrF4JQtxWvnl2yHyVO228muq7j4uKCh4/uCzqWZbI1DB6jHYtFSyKy3w+yMYTMctXRv7xhtx2pKkHqpqJbHfvhwNIBDvGKzoFWns31FcFnbvoNOWmUq9juxXH+aLWQ4iAkmhxZdh1tXXN5fS0ASAxYbQ4o3nq5IkRFDHuOVmvatuXsdE0i8eGH73N9vcFoy+PHj/nOL32Hfuz58slnVJV4sIzjSM6KLx5/xm/8xm/Q96f8m3/zb1muWtr2lI8++ojdfourjhmGvegGS0RaVVsWiwdcXb9Aazg5OREZDQlb6GF+nA4U4tFPB08d72Wy0tyJ8Pv/y3UX4PnbAJ+cigvk3d1DODkl2FQunTU5yuQ950y/3fHl4895/Pgz/DhS1TJxHccRhcXWNTlZkpaoP2ukyGoaYc9M00TTtbgo4E+7WFG5ge12i9OyQaQIWtWkCNaKP8OsTXbOiOGhvp38qJL+YG11SECr6xqTDUrNNPTbpK22bQ+gzRyVLhtTOEyLDuh9W6GVvTNp/llDsZlGSZJGJBcmTrHzxOAIas8UZxPJiA8jOUaccaDkfg/7PTEozk5OaRoxY5S0OIm2rKoKNKwWHd/9zvd4/MVnbHY3aK3Z7LYH1N+95rVotOEH3/khu/0VVkXeeedtDIk21pydndPaFZO9JFYemya06eh0TR49Gsf5Ys2z61csqoZvfe0b/B9/8q8x9YLz9TExXHFyvIKqot8qVAzYyXLv3tdpXMvzV59wcfkE01hq1aCNpl10XF0/x1Uw6IgadyQcN/2G41WD3wUql3BNDRr6aY9PPT54XJV58fIZJ6dLvnrxASlqQjWx8QP7qz2nqzOq2uL9lhwnnEH05Hqibhcsj0/YbhIue5nqBoiTBWdIMeH9RFQaQ4RKiw46Jza7nhQndqknhYBVFlc3KKcIaSLkJHt3v8NVR5wujri6ek5zfITfT9j1ipvNp+xfbFh3CxpTU9kGlTU2GjqTiUEo4GOORCPG3CEmcp4I2ZES+P1A5+AKoQGHQdHHHm0ybz/8FhdPn2GNYje8JAwjk16y3/Xc3Gw5786wyvDm195mS0D3hjDscO71RunGGNnt+mKImOj3I3Vr6bolbZvxoWe394Q4krJHa1Uo6xOL+lRSl1IgpAgktPECBqVIP2wwBlIIGAoLJqcDU8ffMS+lNPCzNOMw1Q/SIFZVjbXmb4DHSil8eT+5pGZIuqMA5VYXxoaRP3jvaawTZkUIhamjBChS5iCJriqZMo/jyL4fDrp7RWlWFVD2zdpoKmVwWRFQ+JTos0TpCotEoZSlqhRGWYLvUVkfJuW5mD/CrSmoMWKQPA8TrLUiSXUcCtBiS1fo6aALM8fZOREyYE3CVhYdi4cMhpAUFKPuGMXIUunZt2xCm1kSUxhEr+nKOaPVzACSs0AZmViqnMQ3ICSsFmNUFYTtlH0mM6KUFrAnC9NLE2jMmpAjm7HnneaYd5aP6Jpzro1lUb/Nur3HePkpjBvWP/rnuMIT9d6jlaV2jUzii4REhhEWhZytsym8tZYt00EeP8vInRN53hzYQUxYE4rE5oaqqiR5b5rY7ffSiBjD04tnVG2HtdVhcDJNI0ohJsTegNGMwx6Dx0fQTGgjxf3oJ2otPiLZKqyxOH2bJCXmxRYNWOOEzaQUWmeUqUSWrSR5SOvqZ9blgVkEhQGG+EIURs4MTs0ME5CySUWwKNZHZ9S64r0//tf8+q/9Ez7+yb/lkw//L77+9m/w6bM/Y0AzxQF0JOZMIsJsYP2arrZdEJPiiyfPcM2C3/zt36H9qyU/fu89/uW//F/45JOP+Bf/4n/k3r172FqSOo2WfeToaMXxUtizodTO3hj67Va8MkrohEae66rUIPNzjVaEkHjx4gXb6xvW6xUPHz4kRmETH5QTSphOVVWx7Dq0ysw5RZcvn5GfPedbD4/ppyDeXUFqLZRUXB9/9ilPnjzh8sVzjlZrTFQybCGy7mr8sOH65hWfffklP3n/z3jy5AkfPf1c1rBVxMlTW8OiaQh+IgRYrsVaoq1qMbKNiTFEfAgEFEFlqe30xMl6wWq14p13H3D//hqrAjeb59zcvMIZWT/LqaFbrSENVHVLt1gxTjIEcFZxcrSGFND1imwdYxJmQ1QarGLRdBhnWS6X9KY6MDXm626df/gzZeh7Z8EJS12Y7nVdv3Y5lbaWh++8w3YzcvV8wxAiBsPlzbW8ZwOrtqFuazpbFc9XGCNcvhjZ9QNXmwl0RVaGwEROEaIk0yqypPgZTa3kYDk/PWFcdLx4eSHptpVmN4ycHHeEoMjR4RQiafaGiKLqVpw+fMD1lzflPmfIcp5nbTEhkHwgq2JybVqUlr5gGAZ88OzGAVPO2ZmdlhBmYVLiF6cKOzMUBgtaURd7CaUUxICKCR/DHabxrXRxBpmdM3JPCpMVwFUVrq4JKrNuVnztG98kacN+nEQimBNJqZJA16Bcy3YYuN7tWa7OWChIUWF1xs+JXoAu9U3ymTF5xiEwRemRZZ/VNFUt8sRSFzWVFT+1ILJBrcDVjrqpaJsGVCYnfmZN/13XL1xd+hjJ40iYJlIS0zmAumpECmJN0cLtSFlRFcaANeowcakq+bXWClVUa5nq5ySbnp/2DMMg6ThFh2u05YsvPiPGzL1799hcXbPdbonFMKyuBeXSBirbHAAKoT2V3PecC4FJJhUSq5bAaHK8TUTROePH6cA2mZOlZgM8cTJX+EmKzxQVIXhhFBRtbAjCBJi1pNZafJnkOFemUkbobdZqXF2B98SgSRGmaWAYa1xl6bqOV6+uICuePHnCr/7ar/C1r73DBx++xziupVkvxcj11abobedkDPncXdcxTYMcTEb9THy0NRV17crPmIjDQJMdzXJxa3TrE5vNpphWSoHinOOtt94Sr6DrayRp5h/m+rs24J/3wLn791w06HdeDQgh/tbnWKiNKt0CQcMgoEsIE6RKHrychVKtxIx4TpIwxpTn5Pb+yPMp0qh5E1LK4FzR6Cth46QoCVZkQ4ojxtyi27OBnlZz9J4jZ0lkyTlK7N2daE9tbzc6W0zE5o1w9kEySR9AnLnwtrN5staH52i+L/M/d+++1hpNoWcisX/GOHAK7wsNGU3UMPqRVKZ+x8fHpAiurg5ATBzE4HSz2RyAnJg949STiOx2wjDz3hcEvhKp4eukx2bFsjpn3CUqFah1TZ+uaNsapxsUlqpr2AyenCyda9E54cc9jVpwtF5zvjlmGHd88dWnxOQ57RbUdYW3HmcFbIm6Ik8aPWlMY4jRsx+fsu+vcapBuYrWqcN+kFLC50CrMlUjniTxTgNprWjFQwqMYU/lHKhITtIIX948Q5kG4zwhTDgVgROUjiiyNO8po4gM4wZqUPqIjCUr0eanhDAldEXSWbZbVaIoSxxlxuCDJ6eIDwL8ZzJJcTjoM5GQRYZA8qTYE7Jn5wd2yXO1vaSfxCzStkuJ5dTSrGmk2Rcfp0RQmVSan5gSMY9YUxNzLHptxUTE6yReI06jVDzs/bYxWCWCQk8QdoeX6XxM4k8QlMIZS1aamF6zDwkw+VDow46Yp8M+NFPljXbE6AlBoY1Bylx1AAN0FoPcnDNOSbOttfg/hSDJjlpZUgqkVJq6fBuhO/99vlIBh2WLDOQsDD6Zjv5Nv7KDmWq+3W9i9LKn5IROQIaQtWTsFqP9kBMmCS1cKznrRcZjDxLnEITOPac8UtYYyJ5mjaU2IjetIocI2DFGYsoooyBq0AmVioQrG1R5rVaKqNSBZHC3mbh7GWOKF85s2Jju7KciJy+vxFh9uL9KZ7QpPg5aSVpNlPcgbIpAylpKZpVQWhp5AG1er7RPltvtd5jIhTWlC/DHAWgzIaO0I2ZIKpN9JhUZr06gtKIxFVUWGv5aVZy6jl4lhukF69SR7YZXL59i2iM61jgF1gntXaRs5mfW2DR5UgoYUx9AjxDk2ZmKb+HdumE+M2f2TlU1ZT3/rARp/h3OOfwkEuHLy0ts1TJzoeZzN0aP07IfOlfjtGG7k/Nj/l3Bj+gQqbo5XVKC5TUabRy5DGiSEm8mDYd6TBfgsAylD8/VPPCAWxDxbn0w//lu7PgcbDG/f2MMWWem3Q5lLAvboVPm3pu/TItn3L/k7bf/MZ88/jEeMT2V9Eppml4nE6eqa5rFES8vrzm53vCjH32D0Xs+/fwLnj59yl//9V/z+PHnbPe7wkRSVNoSc2bRtiwWLfhJzFCzJaZYhl2UhS7XDALC7bBrvpchZPow8vDhA9bHR2w312w2G9kHBTmDIIEYcwqn0RC958Wzp/DqBeRvFy8NIClihLDviePA5vqSm80VxERtNPsMKkWpN8LAzasNF68u+av3fsJPnrzP9eUlQ5okHtkabM60WrNUDh8D4KCtsFrCTJIPxBxpKstoHIlMshofMzEPrNY160WDc5lx2gjg8OopfWHxn5+f0u8GDJG8WoAxTOOONJ812uKcQdmKHeIjlp2TcAAnjHi0ABNJm0MYy8972vz8fqvKWZDVzMjJRVL5N/uC13dpNpuRzWbPfpgKSDzX2Zq5z89gAAAgAElEQVTKKU6OjulWFeNujzIIK2RKXF1umEbIsUIZsbOgDLpUOdNUkVNW2qAtTFPAaHeoo8XjSDEOin7sylAmCiCYyz6BRmFo2xa/XLLfbkk64bJhLF2SNg50REhPs7y6DHmTJDGplEkEtNEHFYgymtpWhDT76pYzYmbU3Nln5rNOK4PO6cAavNuDzCC0MWKYHlU+1DwHf1uVOT47RStLzMKCWVUVyYs/X1QKbRw+ZV6+uuJmu2G5aohGiVrDBxkyFumUUrNpuZhB9/1UmGBGPMnyrWeP1ZLEapQjqcCcHOmcpm4qYQs5sVfJKv291+MvBOJM08TVq1ecn55gjGaMI7aqUF0nPjNtw2634+OPPwbg/MFDFithbEyjeFVAYrlc4lzNet0VtOqI0/NzbjZXaGXZbrdM08B2e0PXdRyfHPHs4ik3Nzecn59ydnbGxx9+hLUVlbLEpFiv19SNY71eCzqtDYFAXShkw+DZbDbFaFlD9rKgwkRb1TjnZCJUNG1yQEtDOG73uK6hdhUhCUrW7wfGXvxMJPrXE7OhbRUkhUWhqkpilKMXA74EOYohbF1bYcB0S5kspoQfJ242l4x+oHu+QOnE2dkJR8cLXr2oMbbmiy8+43d+9zf5/ve/z/sf/BRrNV3Xcf/+fXaFEv/Tn/6Uo6MV3/3ud/nk4y8LHdhiTEtKkXGUDbN2Ff04cHNzw/HJmsViwThapmkgpSR+OFtJ8Vq0EvM+GxxDae6TuNU/evToH8wT5y7L5u9zaa1vNwFjylYUy0RWCiRh6cxTSwUp4X0SkLDShwi9ulqUaa4Ad97UVFkAGJ0lGcJhCYC1EWsNUNF1HSEE+kF0/U3TiJdL1JR8PHIyZQJm0cphjKOqGpytDtPteYPA2EKfnj0QpCCwWh8OOuPku40xl8I5C/MmRzGd1PZAo56NHJVSBwAnExE/hludqrYKo7VIKoGkNDGJF1TKSYDeFJkmT/C95BM4hwwMItM4HoDStkQaCmg4ErPoRuXZlr0lpcQwjpJit9tycfGCN954yLJbCGA6DWVzfX1rMaWEVcc8OD9BV46t73m5v+CN++9i4hH7XY+qIwu3EhArV1ztL9j0L3hw9A7GjNw/r7ncv+D9Jx+yPjnlwckZfd9zfv4A10Kgpx0006i4uriiOk5M+inkHbXxmOw4qU+JAVLI3F+eo3Rk43eEuCdPAWdbjEKMX3Vg5y/pxy3jtCVHw2q1YHN9w7JrCcMNVjm0FmlKGicWVVUAM5ledLZitxvJHWynl/g0UY0LThZneBvompaQI8FXIlvUCWvEoLsymn3s8WO61UiXp2+xWOJwmLph7Cdy8oxpwhNIaiKHnqeb56R6YjtmXLVgf33NwhxzfHJKY0UO60xN2y6ZDPTXL3DdMb2fxLguG7wX4z1rLX6aUEkMZI0xrKqaYZq4HifaqsGSuL644Gi1om1rRj0ykdluNjw4fojNjjBEtDW06450M1Bpi2vrQmt+nZciKU1C2HO26shJTPxlOgzWOvrdnpSg7RpUbjHa4L140BgFPgdiLDHxFeQioRz7zQE0Dn4UcARNjLMpqhRX4gVmSSkS4iT7QchFauQRexZNiNMBdLwFbRJK5QIazTHjE5rixeCt0Kx1Iw1/LnRspZhywlmHsbaAwbemrSEE8SgpYLLsb6UhLYwprRWVMTg01kqEslKJGBRZaWosRPEaIwSh4ZtWfJUiSAF96ydyKEJzLkdJKg10jdGajMc5W8IhAvKWNHmazR7LNFmDNhltBKDHQI6JSoHKAhympMhktAZjlUw4lcbYudDNf2+t/X+WK4NVhqQEMM2zt1wSmVfOUdgNoRHPF6vJ3gvgrLzEsfqIpxTKOjG6zJFtOGkbLuLAvVjzxvobTNMVrXE03bcZppHVr/4zDBMxiteSUgImaius0zCNkDXWNCSVCAGsNUwh4spZedc0c5Yrz3IrYZNVRGPY9nu6rgOj6adRhjQK2roFlQ5DCO/HckaX+sMa4pTo+x3r1ZkkQrYdIUhDVjlXbOoCpmqI2ZeAA1t87sxhnQnrSaON/pl6SPCDIN5KKZTXymvuDihngMaHRJwEwMpEKiuSKXMnXv3uEGgYBrRS9OOe6vSUH/9vf8C97/wuZ+/+Nv/xyZ+yu/gPmPUZ/XSDR9LUUh6LR87ra5xPz875b/+7f8Yf/MEf8B8++Ij/8r/6r/nBD3+IUpp/9a/+V9577yf8z3/wP3FycoRWlrOzM3bjRMyJ4/WKtnaM0dN1HTFGrrZXZTCMyNxKLdw2DcvFgtj3DH7CJwF/rbn1K3v06BFt2/LJpx9JElkJiEAJ4Pzo4UMePbjHsnY0GnTIfPXZB7yo/pxvfOfbVPfOCaPYWOw2e3SITPueTz/8kM+//Bx2PW3bQpx4/OQLXr28YLN9xeNnX/Hk6VP+8r2/5rO8xznH6XINIaBzYt0tWdYVD1fHrM5r6REqATxq68iRw550s5dAl957Ru+Z7J6uXtA0DqMTw35DU9U0lSG3FtRETCOn3ZK2s3SNxTjF4EeyMSitUHWDK8P73J2yXq+lHsypnFuWkMrAgFu2Qsp3hos/1/yDbHmH8ULxxLHKiPw3ZcZ+OABtr+vy3vPx46+EjWkrKrdCqUzTOu6drahtxmcY+z1NDS+unrO5HPHRklODcg7jIpEojGI7YU2RU/lESl5SEsn4KPuDjeLtdtQt6UOPRxGnkZcvN6xXLZ21hCBG7ViHyULcWB+dcdJ4Pnj/IwITRmkIHp0y1maiE4lrSiKTzDnhlKaqDbXTuGZFCIH9MKC1RH2jFSorAbBtPnyHDpEWZYUMw7Pk77VagJCDQfWdf889ywFYzuJfOvcTxlmyMcTg+dGPfoUhT+TkCMhwqDKZMcwJxIrPP/2CXe/RyrHZy16flMJEYYNOU/FPMw1DsYcZ/SRsqgS6dlKDlLCZysrwXSMD55w8dSUePE1TUzWuJHh5YoyFJez/XuvoFwJxUkp0XUftDElV6LZl2OyEoVFZVBQZ0tXNNTpmjo5PcUpoxfMD9urFS9arU2ytePniBS9fvKCua3bXNwzGiGFx13F99YppGPnd3/0v0FrzR3/0f/LNr39Lmt9BjILrWjGm8XCYPn8uMdj37t2j73uZpK1WLJdL9vst436PNhlnqgMd1jYNy7Zj8p7dfiu571qK39pVLBYLptGTg2IcJ5q6o7EVN2HPzc2WuhYTr9p1DHHEZoNxBl801ou2K1HSmbp29KM0QsvFmm2/JUwD9WJB8hPOah49kPQXa4Xae3Nzw3K5xrV1kdlE3n//fe7fP+f73/tlPvvsE548ecK980esVkfsdjturrd4P/LgwQOUynzxxWcopTg+PsaPE2MQp/dhaGhqoWlvt1tijKxWKx48eMAwDFxdXbFYLGiahq7rMEYTQmLh3IFefFHu+bwJv+7rb0Mr/7b/7W+bhuacUVGSlaTpSGKoOcuPKFPOLJtKzJEQPeNuzzTuqDvZ5F5dPpXNUmlIhqoe0ZUm64jVhZOShII7AzJNI55QSufiM5MZx5aiOCqT8gyjsDeUhrp2VE13SIjQ3hf2jsOaCp01GI0qdOiqtmLwVlg7rrYHeUJViTxBBTkc52nbXZPtWX4Qg0cb0Ng791Ic6sXXKR0O1XyQYsnEfTYJTTmgAvg8HlI9VJF11XUtE544x3COmDIx9lEaunGeQivFsluRQmYaA0/cV4yjZ9HKtCuGxHq9Rr3GpZjyyDY+RucltW54uX1CVjUrd85x+yb7cUdvnhB2gSnCzTixGS+5Hl9Qj45ua9gMz3jZf4qvB05OvoGOGRsz43ZA1Y6YR46aJbZZ0ueJii2KAdvtue+PMNUKNcE7b75DGCeunCPlQLwcUNaRNawWNdN+QOlAZiSkDYqabHva+iEvL6/BJ6xWdO2SuEn4aHF2hXUVy3oJukZrA1FRm45UwXP/GNd4Uh7w257kDAbFqlvhdWQzjdxcXpErT3CByjgMhpRhGkfZP0pTo7NldXSEszV10+CjZre5oY8jWJFDaSKjuWIzbsTYefScny85dl8nE9hcP5ciQVmWq1P2eeTVy4mmrtiNgQqZiIbeE9NAZRp0LUkWi6altqB38tw1GRbLJTYllI/YAoLsMkymxnWZm5sbjpoj8UNYNmyGLQRFGCd24fowYXpt6zFlFI6cFd7L86eSyFq01qTsRQriizxCtex3ESHkjGhVYRyMcS8gjq5FNqISKRoxmEYimEOYmz35vbn4DsieIGwbkNSnWdbprMFYhZ8mjJazeJYeyPsvcdxFEoTKWGfwky/Ft0alWDToGm2kiNdaobUhFO8aZyuMNlh3y+qR9CaD1vEAsKR4yyqQ3x/JIYKGYAXYt1nTaYVKGjObNOdMzgbrDINPYr4dCy2biC7ygbsGjHFuQGIUhjAil61rS0yjyMSUKqyvKN9lYYcIK6PsvbkUeEo+tzGKVIZB82cRdm2RlVEafF3MSF/XNe/bGFJJFFPF7iZni8jtrJyT9VTqL49Kmkwl7LwUQCdSjhAhqMjT/iVt1fCri3ewCo7HDbn5Btvdc6o3f8Txo++Tw0CuqsOnTWhsVYnhZAzENEu1pOBXKTP6WzbO/L3Na3MecMxmlvN/v76+PrDJY4w0Xcvl8wths5S1nXNEuxpDRY4CqMzfU0ShdQNG07iKpIQ5HVLPbj+yWh0zs8VSAFUSHVOYIN1KnLUVoCqnfDijgSJZtLL2rSWnXJhxiNQrO0ypGUDY9DHPUhNhG86NERTAojzo1hi6uuH5sws2m2ccn7zBcn3G9af/N8oYmqN3Gatn5KFHpxGrNAELORHi6/Otk/vg+O//+f/Av/uTP+Xf/+mf8Id/+If8/u//Pv/0n/4TnnwlbJy//Ms/58GjN3F1RQie1aKh7lq6xh0kmeN+4Prmku3UH7xW5jQ9lYucqqrYDT273V6eOSPshJgyXdtweu+ccRy5ublhmIrpbpoZYnDv9IRH906xOuE0uE5qpA8+/Clffvw+3zw5Zdj2NNWCOEXCbsfjTz7gg5/+FRfPn/Ltoxan4PnzZ1zdXGIqxV+892O+/OoxwzBwfOQw1SOUUrTOkkOkVoaT42MWdcX6+BSXIaaENlLf3Yx7hl4izKcoLGgfUvl7QBlwR3C0XPG1d7/NO298TYCknHn27CuSn3j46AHnD045u3cf03QEDMuTe4xwYJ1R/uyqRgzgZ+acUozeE3K4ZY+HwiC7w277+X/PMirNrYG6POsjJDkTvPeFtfkarxRZuiQkgsZRL1bU1mCNIgA7D9vdwH4YGCZLzhUY2ScUoFKibuDqZourlizUCjIkkpjCi2iRXZhAK6w27L3IP7XRLJsVMUd6v4fB08fI24+OqVzNNEryY8oJ4sgYLW3zDscP4ZOPPkRraNoV2Y8oNdEmByFKXaUM0VimEKmKLcKw3UrSLpqFrclVI8OUQYbUVTHPno3rM7fJXQBNzGRdoueVBpWJ+Q7TTUV8lFSr2lWEaU9ba1xlSQqGmHHdEd/7le/QLd8gZUNMkaWqCZViwjKRcNWKL55d8HyMWNcSvLDDcsqQIkdrS9sYrjdb+u1O1phzxORF/RIVGYVzFWuncUbTNBXyrURZ0zliVX0rT60s3idh1eVI8Ak/hL/3Of0LR4xba+Ww8p7oZUoWQsBYYRAMw0BbNxL727ZMwd8inEk8LoZhwMfA9c0lc3z3brejqiqGfU/MMPSBB/fuE3wk+IHj41NOTo6oqoqX1xI7bpzF73sxbc2Zq5trEZOTCOPE4CcWTSsxz2mJtddIxFkuk8EiZancofiJUdIrxLAtULuKuqoOTSZAVkqST8oi8lNEq4DSIuewGqIXyYC2mbqWA7ttF1jr8THhKkXaDHgvPwsjB/BsiGfLhGXyAydHx+icCCEyTSMvn7/geL0SgGma6Puevu9L0kM+uIJvt1uWy6UUu0VSADK9CxmGYY91Ha5y6ChNu7bqMG06OTk5/Lw5gjqEqUxHZ9qXIhTWToqvF8m+e/2n2Dj/r/9tjsEuvjh/W7t1YJ0gjurJB8JUUhZyIIRENkFAi5wFHEmKVOh7BkXUkHRBqlMmFAq2KhIopaywV8rvMtqQZpf2FEE7lKkPmvRb+qg8P7V1xJkimsFqVRIqCt3QUKh7NZBwRdIom2A4ACR3ZQxqpksepskRhTsUh0ITjqC1vNesyIQiP7hdC0ZZskoiMShr0GqDMRo/SVGQfMDHEkGMPC+xGH/Le8r4MJFipkbT9z1DScYIIRBSpulaKbRc/VqZOEolruNLsguYQWjUx41E3PZ5h6oSLef4eosxjjG/wG0T1VSRkucmvYAcqOOC4+NTrqcbzOKU/aip8kAYZMIcoiM3lqV+kyo/JsaGZFY0y4AyLV27ICUwboF1W/bDNW29QBmRWi0bMRT2MRJzj1NLFNDVJ0zJ0XOF969QPvLG8RnKBmrt2fcBa1fkqSKmhFKJEDXKTRBvsH7CxQCxZxees7InBJW5nLYkAp4NXm9QKIxXhLonothPklKzi2KcG8fAUVdT246qXpFihTOKxq64ue5pFjV9mJgWljqsWbTnXGw2ZJtRi3sEO8IUsEnjjMU1NUolzBg57t6iTo5l6MlqydW4Z4xSKE4hs6hXLGtDUzmGcaTXkLWnjZGaY3zsSTkSYyBN1zRqgcMyYamtZRo8tckoAnsVMVlR1ZEQB1J+zYaJBawgGzGDzYX2nhNWKbJSxGwwJcZzPw40dVdAFpFwTMME2aKzIVeBcSzFUsyQBTwBS86J4JOomXIkBzGmBdibJJp8VaGVTG9N5YlR9OV1XaMQ+jJKiUeJylidiakV8IkgLNnoqZDiLeSErQ1RJ1ATkg5VlTpDQA1rtex589FHEqZQSoSYsQmJ5rWGMfeErFnntgDJGaxhShmnhJmrlcIpg1YKmw2uSCR8nEi6yAMrS7SgdSanWUIr6ZIHuYoy5JwwRpd9VMwnY/LCtCEU8CYxl01KzWdBAXF0BJVxSYZDIQemIHtzVIHKWpQpkt3sRS5nZZ8PXs6M13mlJObQ5S8io8yZpIqUOSm0kaQyPwkdPuLLWZZEDlnatEpZFtpxtjrmVNd4kzDJ8sxUWJ1J447qwfeJ2Ui0+Z26wBgjkn4fyD5grBM5WoyEGGUIlW99Fu7WDHfP3ZnBMg+yDkOeMoRIIR4aeWHJ3J6hWmvQluiDsJjvMNCsMkAkh4TtGtKkqQNwpyqR9SxgQfQBXYun4gw0zd5R8t5v5QmHz5Pl0fVeal8/RqKNdE0LyM9IMr76uToDivbtFnAtAJitK4lUz4ZnP/kT2u4chsx08wHT7oaFbngVX0EyaAVGJYJGmGyv8copUTvLN7/5TT76+APef/99vv2db/I7v/VbPHz4kGEY6PsdLy9fFdZcomkr2qYqHn6Jrlmw32xJWXxaBLBGTFGVZvLToafZ7LYYow9MgclHNLBar1mtVuz3e262W3KCFDO6qsj/T3tntmNHll7nb08xnCFPkswka1JVl9USDEuWhwvDlgAZfgK/h1/D7+FLX/oBZN1aUssyLEN2t1CCpC6JVWSROZ8pIvbki39HnKzuFroKaLO74FgAkSCZ0zkRsff+17/+tbJC54SzhZxLEaONNPEqy7N6xXG7pd/tMNrRHwfioWfYH/ji5ec83F6xaBxN03D7cM/DbkvMkbvdFh8HXFPTLBcyQhilbiJHjHE0VV2MpxNX+wdRVsTIoTsUNYyYJocQOByPLBYyZXG/3YpyG0fzfMP5k/c4W12ibcuifcLV1Ru0aQheEaLj7PI93OoMt9rQ2oZgpOEA4KdHLhGQkWSttZy5Y5QxWSvnw1SMbpWS9Xm6zpRGbv7psA4Ayj5AysVHUc7o7zYrDbTRrJcLaZA7CQxRBkKC/b6j7wL740G8Uk0DOU8JuhpZS+q6xmqDTo/GkEbV4RhfnjMqgS8qj7quMc7Q+0Ea1tpKUuLg8UPCutHzrWzzWqOiImfNoj3HuAYfOnJOZGXBBJQVsYaKRWlLFmWrVuSQprpCyPAg9VEaUxUVSjOtnzGKJYSSyEi5fkrIkVD8d2IIxFKDjWvz44aJcw6rpRlUNSsSjtV6zfr8SRn3Ko2bBF0oRxpXEUjc73dTZHoex8pyJqeIUZFlu+TJ5oy31ze8evOK1HusNgQjJKG1jmVds3LieSrkuEZlsZgY96EpLUuZySTZD6GohOGbSma/FYljrUVbS9cdxJCnFJBjctEwiPnt5dNLVmfnuLqafFTapmHnd6wWS/rBk4YeP0QuLp/inOP6+hrnHH0vXY7tdsuHv/YRD9tbDocDn376iTC/3nN7fcPFxQXDENiFLVprDt2R/cOBunHcXl1L+k5WqHZB29YYBbtVQ86K7V5UJ03laJsK3VSgFUPoOO4PxJxo6zUA7XJB13Vsw44UAmGQ7sFitWa13kxdM+89zVKIn5wVi6YmhkEOKGOXryT2OKMJgxcG0orhstHSuT1sd8JINxVnaYG1mrONeNP4QQrXt2+/4tmzJ1xcnrNer7m6fsvN1TVX/i3tasnz9y45dpnXr9/wj773KZt/+c/4iz//n+z2D1w8uSjd2CDS5ZyFvNEaa+V6HvYHbOV49uI5x+1OCnXg5uZauvD6JA8fi+m6bYnxXY8NfB2PFTiPD2/jx8fpCqePIyEh0j5xQBOJe/kPtC1eQVEO0lEVL4Ukh+r1qiGRRClT1C5KR5xShJzLiMHpz0gGCilqTibCWiJKYTT/ymhlsLWVedCqoq4btFbT4hKjpFLYSqOCLOwWiZ639Uk5k8qG5pzBWHdaQLRmv98XHxlYLFYsFg0xRvreQ1l0ZPdQJdmmROOVjuB43cVKLWFkXmIyWw5IUkpI4kvgbOlKFwIYwDjHUDyoFm1dDpie/bGnrmvWm7Pp+bi9FeO3qqqoXMNi0ci9uD9M7+U77akEjQ2Bm9svaaoN3//gX3A/bKnqBVlHcvYkjtiFQgVFi+FprqkWa/QhMfTbQpRWoAw6Oxn1iR3WeInfNoaFqQnKklOgqi7xyTPkLdZ4rKtpavGjSTFRVQ3WKVabp9hmLX45bc3+/obe3xKxtPVzcmxISVICSJmhzzS2Ztcp+t5jKk3SikPs8CrTeIXShrZZkoMR42xiiV+WOeDdbseQI6EkoNhj4LjvaBY19aJh8HuR0RoZASENKOVomprNusGYjFGatlqhUoA64tx9IRUN5Irq6fschwNRHxi6jgtjcd6Qs8KuzuX+SRFDy3qxxKji9xOcFBD7Hh/KOKwTMsPWFUobTJWwhyCHp6hhsZARiO5NOUhEFo2jXa6xNpN9EnM8BzcPb4iDp+IMpRJOZ8wvYcpUInFTUWpogodU/G5yNsRgixRdlWdbi5JGDVNHTGbzFSmejIlzFlUM6hTlmVJiCGYqYKMtSoCYUUbOCRJ/nYlRfe3QNapLJrPUJIboWhtZZ5SZxlolpSLL2JFSckAs+1BWUmBSOrTOuUfKGolAkoZTKslZkZAyFoU1MpaSTTm1hjx5R6ljpFEVFkWdNTpAnR0qiVJBJ1GUBTWwqCyHLGqhZlFP++b4saocOYr60LlaRnYBVyl5T0n4cIpknkZktD75mZji8ZRTUTCJ15MuB9P12QpDRptUyKQEKpR1exylenfFSi7vf9IJbUS9lbOZiracM8YJ6UXOOAch1GJOnR0pBUI8SkQ2WppGTvNm6Fm3La8OPff5K57EFnP4jH/7r/8jRqWiKiv7PuLlNnocjs96TEzNGKtPSpOpWZdP6U27w35q2gGcnZ3hvZ9SrcYCBZARdGMYuh7XNqSssFaCCZxzMj7oKlKRy7u2JcdExqOMIw4DMUWaqqGuGrquK0SApHblnHFWUkUTWpQ3WchOiawfCajRA+/0nBojZxxdInezkj347uFePCgx6MoJ0UqSdR3IKUpIyU+MM0QFg/e8+PAj3r5+iXOO/dvPuPz4d9Bpiz38D4JOuMbx1kf6FDhGDykRye/UEyf6gdwf+O3f/JTXf/cb/PGf/BF/+Ad/wOXFEzabDctlKyPZUfyynDPF57IjJYpZdUfnxWg/Mj6rZYnKSZRUKXFzc8P9dkvpnpGyFLdVbfn444+5vb/j1atX9AMYDW0FMQ+krKiNIQ6iljda07YtTdOiXMfukLDxSOgPZNvQHzK722teff43fPaj/8V7lyuUytze7fi7L19ys9sRyOSh49B3LFcLWufo9x7VDZAzg/d0MXLHAxhNTIkuyjqmtabK8VTnHTshVpUlHa9LsWxxWnPhnvD8+fd48eITlFkRYsurtzuMWWOcxeSOZvWcY3WOaTZ405J1RecTpm7KVRpJl4Qz4mOkNQyId3QMP99f7nTOf6TAL07xohRP8rxlNfmdfRs7hl8UnKt49uI9hgDJe+6PR+KQ2e+P+FT8pwxoV0M42Sc89oob97px/Xn8cfy8oSi9xj1l9Lv0MaDMqVbtY8/t3Y5haFi1upxHo3iTKfAE1k/P+eDXPuL2+g3dwzXOKqx26JTxI1ETPDalyTs1Z/F+NcWmZPQ/yjmjrJJnLMZpnzRGMfrkPfa0iWSGIcjzFwIpj0Qzk/n8SKDatiaHDl0tCWmgXa35N7/7u1SLJd1IjCtDrxNaVdTVgtvtgaurr+gPnmaxIhT7DAkXkH3cIOEiVb3gk48/4HufvM9ff/6Sm5s76pw535zTtCuMrlk4ByR2/RGDNK2SUaioikJZ9ppj10GU8brjUVKw67r+xmvjtyJxxg1s9I3RUZGD/5q51Gazoaoamqah9zLr3tYNqhz6FosFKP+17zVeqNHwKCfF4DsALi4upojG0SR4NDwe02jEp8FjrDBwnR/EGLaYKw1dPzGQKYHaQWVNkV4ZkpY0B6slfUvrhKsrNIq6RDgLoy6R1Low61VtJym2UiJX2/dHcs5s1iu8EpJjZOK7rpOOTzHXc8birMhwMaIueNjekZVirc/RKGKLo1cAABXPSURBVJq6JpcNxFohfrz33D/ccnF5zuXzC16+fEnfRfr+KItw8XkYR8ZWqxXL5Zr90GEqw1m1mlhPMVaW6zfOR1dVxRA8h8OBuhJZaQiB47FDa81i0ZJLN6brOvEzWS6/Zu72q4J/aIzqpz4PkfH9rK8f2WFIhODZHzqRw2tJTfKD+BmM9/KkejIio46cyK0xkSRMMuvTmJC1llhiT0OIsslojSkmeVqdnhXpulUo5TFaY61Iz8XA7ZSIAqCNQY0/pxhFisRdT6OIXXcgBFGhpVSR0Ogypy1z35I1pZKRJDaNdAZyRqkk5NP4NqswKZvG5KrxYCyGlpkYZMECWbRFTSfPiatMUeOEyeE9hcSibYVjU0G8gZzj5cuXtO0SsuZhdzuRku90hCVrTFCY7NEkcjL0najzbBVBeZSOMtdNxLrMYlmjnBhJd91AShBSIuKplS6JPwGlokQwKkU2HqUrUgxEnGidzAKtjqAVVd2SoyYWs3adDJkKZxeYqqGuarzzoCJ9OKKoUKou161j2S4geKx27I6e0tMiaVUM5AcSTsyASWSdSEZUDzllNELwPiYrY8ioYnAcoygbQAxNU/ZTx1eTsUr8aXPxUcpZiUs/jspIootVlhTgOGS6kMBYIFArVQyRFcrJcxyGXkYRjUarClTA2UxIkqyVYo9KkthilEFpS9SiojNJE5IQGCkbEsXIzhRJd0i4SsuBO0bxmyh+JkvTYLOTpBL9bs07p1syjx4BWTpdajQbFNVcTqVzVoicVBRy6WeYBpIMKXjyWHOZLHLnUtSmkAmjtDllQkyiQiUWJU5i9I5AnLOKXaIipjLWNIZelNEWjCprAKJYMWByACVeNNO/I0bSSomaI+dTZ2460CpT1qA8KYd9lEOgRe4pq4So1sih0BQixWFxylChabJBpYhLGoKQ7CYrssoYnSm0E0knTOVgOEn8xz2ncobKGUm9Gcd1dJSzCiXhL6bJlDkjBeK4lufElPpHEt8gpZGOHwpnDLoUQlolUhLiRK79qI58x+N9Ssg5SpMiJ08uBQYYfPLYQvyP0NqSzSCd2OxQyWOkb4AfIufNikPUbIevUErxmx/8Yz55/q/Q1oOqS3JZMctUcs+PI2mjqnjcd+XnqWmPetzkCSEUM/00ET7j/jtdk3xSpj4mJ+u65tj3GFPjtJ38toYBqtqQY5iSrrIRNVeMEVfX4E9d5lHxk5KMulKIpaqyRalKIceyRNXz9QS001qA3APp5BsxKojG5C5TKdqigBqT3+q6nl7f49etUsaXxkzoe+gD9dNPuP7rP+aLuz+kfv4hrlrTh1u63pOVnKHS2Aoy39y88xeBEAJf/O3fEH3HalHT1hUqR370ox9xOBy4vb9jCJ6YilIsBfH30E6eR5Un4i74OCk3hMSRv7kyFnLsuzKuxvTeqbH77iyvX7/m6uYao6Gujai3nKVIxaQdFsfQC4u2FUZHaptJQ4eKAWWlQH/9+ktef/UlTeuwNpJy4NXVG97c3DKkTB8DfeiEmO48xyFy2O1wWVSXffCEKARbNnLtfYpoZ8XDUIvpdkCS+iKimgyDp1aGppK999APGNfSLtegK5LSaCdnMVM31M6iXEU0logB7QBFVHrSBupC4MjeI/f2SMZQ3s6f16R7/PxO5IX6CU+cnKdtWanRAP/dImfY977UUJGuH6aRU6MluTH6SB8GslJ4H6ZactxTHvvIjQTG8GidGp8vWS+qidSJMVK30nx+bGB+OBxQKbKol+RyX4eUSEqSrkKKrM83xNAzdPcocllrI1VQeKVwCkJRltjiCzquvY/rqal5PZoglz8ng+JTEp5SCmtOysrHypsR43WvrcNYBaYmWc3+IXL+/prN0w0+lHNhVhij0UpGbJUy3NzecXe7xdXLQoDJiLmcMyJKS0KiImFyIgVP1VZ89N4LVu2Cr66uGfqOqA2rJ2tUkj0jDh5ViP5xzUWr6ZzlvScOcdqPXLEr+ab19Lcicbz39IeeF8/O0SqzvbkixljmHsexFEUqjNv9wz3n5+cYq7i6ugadWS7PyAwcDmKq1dQLXCXJNENRufztj/+K5XLNRx99RM6Z/X7Pfr/n4uKC169fs9lsePv2uhgdX3B2dsYwdNRtUzbFQSKOc6bvDnzxxZG6rmnrmu1+DySJAC+z5D5GUoiivjBQ2YpF06K1xRmFVVqiHK3GOenqRC9EybE/iCKgxEqyFwniom3pVEmt0pJbf39/RGeoFhXee2wlncWvXr+SuLzjsczKW2oDkOj6I6jzqUtAtuQYefPqNc+fPeWDF+/xt+dPub66JVbiL/D61Ruq2rI+W/L69Wucc/yTf/rb3NzccP3mLU+fPmWMNly4WswnlUSzHY9HnHOslytsgrp11JXj7v5BYtkLi2uLX0pVVZOh1K8KhfPzDgaPF4L0KBnlH/rccUxMkTjs93z22V9yf/+W9bKm1ZXEI+pMoyAFTzABrWIxbhyTT2RUr6oqVGXKiECURbOkS61WZ3T9OKbY4bTElVszxh9qnLNlodNYZfBOUgOqykq3h5KOoMWXRFlT0me+rk6aCDtXsdlssFYUObvdTmSGqrjko0oXVA4psSTLpFIYSHEg1z6XbVI6zCWa1Hti9BMhFWMkxWE6DPruSAiJfd5KMoexHPYd1krX8Pz8vCx+suDVdc2zZ5/w+vUbuk4InuNBiN3N5gnDMHA47E4jnO8AKkO/jbjNCrLlL//mLzBtSyRQZ8gMZDrq1UKSmVDUCrJR5KOmShVJW6qcMdbSZIc1kbo2VO0Cowax3NYBpXt86jn0UsAuVktyjvS+43b7hkW9wBiLqytSMAxdhGwxuqF2G7wTY9Rjf0NSTv69spwtLdv7K7SqZR47gzUaciShxexOCcl3TF5mivMeHw4YsyEnuS+yMpMX0vHQMwyBeoDGtkQfCX1C11aIKN/hnKFtXPGbybT1Ck0tcZQxlC4NOAvKRGzK5JC5u71lUEecVbRNhfGeY+4KOU1RSWR8HwiDjOXVTYNtau4fviKmHZWN1ErhlBzh9l4SF4kBqzzaKnTS9McOdMZQi5G3KRGZaaBSDaat8CExdIGL5XPS3kNc4nOHqdp3LtMeD3dGnw5yo2pvlAjnpIlZxn1yUgRf1od0UrKpJMKUnDUqWYkvxRPKaBXFHDUnIVxMeZkmaSpjqXMSohBFVF4IJKeQVAZZO8hiBi1hA5nHhbys0VaIirLGoEZSA0ldKooDo7WY/ZYkjWlPUqIyAkoqlcyc55yx2uGSpabGJUPlW0noSBkbRXWwbJa4VFKkCGRkDGYYBkKMYsiYEsEmstYYp9EWdM4EYiH2Zd0zVtI6R4LdOTnYDX4gZYkPTzmKmtE4JDdAVBS5xMaO7zvI+6CNFCjWym8o1LyYUwupmsv3PhUt7xK57AMZj8KiTSBqjSKIiT8SqxutgZJyZlR5raWyc1ZDXpSxLFHTvBnuuLtTrC6f8L3L3+d3fuPfiyrCVmgkPdK4Gu8DxpxUNcZZ+j5Mf49k8X+JX09nGg/UY8E0DMOk1hnVOYfDYTr/jIfzsViSpDqKH6Ioxsbxqkn9lzXGVmRktF8h6TlKa6zSxbsGwLJY1MTYS0NGic9RIpfXpmSEWilCjmgFWleMY88j5Otsie8VP70UNZVryEvD4XCA0jyV5pHFKnVK9tIS6DCerbVSxJQ4DgPtoqF975K7z/4Pzz74La6vPkPtt2gVaRNs6pqH2AHybAUDKYoX2rtCd9jxX/7zf+LHL79gfzjw/uVTDn3HD/7ov2EaaVwlNNZJQ8kaOWcJ+SbXdn/sJDVOK2IYi18gnUZcjscjw9BjnKaPuRTDws9oK2fsv//8x2y3W5brRs7R9sj5s6egDfdvr+m7A/v9nnbxMVkZstIkbbG55+7qC4Lfs1yuePPVK/77D/4Ef7jjt379gh/86X9lf3jg85d79ocD26HnZvfAse9YnZ0z3F0L+WIdvT6UhuGYQlqRCgGgtUZHCCpxtqglZCNlDseBkESp2LiGGBT7YWB5ccbTJ89YbFraswUBz5A8T5484e7hnvV6xWLZUFWikFRWimNtYWE0OXuUzhglI2cKQLekKM0dlSOWLP44WQb+xArhp0vYqbDXj8Yi84mwBSEnSCOB8/XRyXeFwXu+fPWGEBIKQy4+WYPPdMNOBAXO4lxD9In9fo9SiqZpJkLm8fpzPB5ZLBY/RbaOZEfXdWJ30rZsNhu6oZ+a/VplnDaE4Lm9P6JVz/Pzc6wr6v6S7NilyGKzZrF0oHu63RblvTSsY6IOEVfGpPb7vSRTjwRcLUTq0PfTPVdVlfgjpjGh8UQ6jb/3+FqyPq1F49c+DtMZ12tbV9QLh3aZh33g4sMX/N7v/x5Xdzdga9AV1llZe7Xi9nbPzfUb7rdbMpba1Vzd3BcypSGmDqUy1oI2icokbJamlg5gCby4POfp5ox9JyNpdw93BP/1JN+c86Qe6oeB/X4vTaUYS6iG4ezsbPJb+6ZBQd/a2LjrDxyP9dRBreuaoT/KL9YNDMNA7wPBp6J8STxs99S1JEfd3NxwfX2L0pbnz5+L+uTBi4LHiTHyZvOETz/9lMvnz/izP/szHh4euHzxgrZu+PD9D3j79i03Nzc4Y/n+979PVVl++MMfysZbiXQrBT8x1c45rNMMcSAMA21tyRmqWhbQw8NWJKtkSXQgMnSHktbkqRuH0gvxvtEanxJ3d3dyM2nF2WaBwtEPRzZnK9Cw3W9JwbNaraiMZTh29H3PkydPWK821M5w97AriRSgC7EUvUPliGkq9scOrRL7vcTzdd3A/mFPs1lTVTU+DGi95tn5hi9evprmDWM6ouyGHMuDbzSLN2+x2vBkcz7J66qqYshROow50SwX1HXN1dUVwxC4fPFsugHbtgVkwb+7uWO5WU8s8KJpWW7OfikL4U/iZ41R/SxS59Sd0kJCqHyycTGpHJTLJm0k1i+Vw/vd3R3DcGS9rErhkXB1I//vj1g3gFvJtzIGbTIxgLVjt05NapjD4SDzyE2DdQsGf0PUClsvsNpgK4fOBu/leJ6ImFL0JMBpjXZiRKgNGFuJKifKrKkubuvjojl2qKffTctmOqYA3N7elk5lKASTYrlcTmbKMfXFVDFKx7h8P2vUpNpRpdNhlCYZ6bznECfiqK4bIJMaR6+g6w8MPZKc5TPbhyOL1ZLFYsV6vSbGTN8f2e+3UoSFBbvdjuvrW3a7Aw8Pd+QUuLx8RtuuC8v+06qq/2dQmeAdOS3JtcEP9+x2R7RTYGtyjoTUs/MDrTM4J4WAw8G6pT2r8EGRhwrXOvKuo64MVtfSlQ130lXPAykbQvIMuoOsqXJFKof47eEViTMau8TpDcY2LJciNTVO5s5TguNRzITLtB1+iFizxOodT59WbI87dsnzcBQpbG0qKjKt0ey7HdokjGs49Ed6v6fWNSY5FLKmxPHefnig7z3PqrNCOop56HHfYWtL7SyutiQvoxS1rdC5oqmWkC2DP5BTwphMTANxOIIOVNWa58uG68ORIXS01YJud2DrPD5GcoyYmHmxOpcUIZVoakdlJRI7BCEJm6ZiYRsqXeEWa/bhCMqQU6LLPVWtqIwkEVmlaepzXGMgaHa7jmP3wKA6XH1OVhrlLd3DQN20rJ98wLa/5+b+79+54fvY2TqpbzKZnpi8RH5mRYgDWtmipgOQtdx5XQxwdTEjVvQ5TR2zkQxIKaN1xhqHQuN8Uf8hxsNWGxpVY3SDsgZlkqRomIgPfSEseqzVeEb5u0FMe8e1pDxeRaUhJqLF8LAc6owW42GjVVkXU0mkOu0Bw+CL110kJzEdrJXC2oqWijpVGDTN0KCRZEkXESIqVJPiMCmNcg6PJ9pI0olcKZKFXsOAx1QOozV+6DD21DE1xrBeryfFRF07cpLoYKvF705luc8wWpRSWoqlFGPZZyIqWyiUki7z9lrH8cJjKydeAkCOEkIto0pi3A9S9L8riEq6Q5sKiKDAKIMvzSqAGAs5kkXLFImEHKi0LlHqGh0t0RVSywRqW/HivY/5d//8P3C5eZ+oZPRv3w9E38uIQT6NR42qrBS+rs5V6dFIT0wYa6bRwfHajappKJHhRSUj4//SQBi/53i2MsYQykiUNDEyXfSoICmugQzWFCVVxmmHSTCU+8MYiZUff88QwqTIkb1NUVcOHweMNkKEZvFmGQmpUQk7jnuFEDAosFaUaClOJNBYEO33e66vryUEw/vpa8di31pLnzNWiRL+5mFL//krFp+8R/YDD199yWZ5ztnqQ/zD57w4+y2OaYfpvmB7C/dqj9c9JkR+/mDMLxY5Je6v33D1+guyViyWZ4To2ZyvycZiXM3Ti2e8fvWW4/HA++9fsl7KOFuMkWMpPuUsDHd3OyEOFZAydduIIuVwYFS7iZF6Gf0sJPr19fXkX5hSYr1e88nTZyjXYlxDlbLUOOcrlDIS9e1ljN3kwO31G3zfEcLAy7/7MXd3N9Dv+MGf/hV/8b//nLu7K6J9nxAjr66v0W3N4vwpQwjc7zoZPVlueNvdEFOUprfSLKtGiOEMOltW7YLKOnSK2CyBH1bXZZxbGr1h8ITO8/1Pfp0XF09JyrPd3+Fcy+bZOa+vv2SxaKnXjsV6wXq9pGoctVGixC3qspzFU9GRJiInmbXsE2XKIVtIQyIX1yad+Jn30GmcSp/IjKL+SKE0YEJAKyvNip+h1n8XSBlJkKykEZK8aPa1qWUcM0XUIAbgzli8D5OaayRvHqtRxnUnxpO6Bk5qPOfcpELsOkne8yGwaGq6/oCPkcYaFOOo7qlOijGSbYXVihiPVHXD5sk5OQak7SDvc9IK7SMmyn3dHztyziyXS0IvMeon9aPGGVFeWXfy6JQ6K6C0hBaokgTs44lkH9+DkTw3xkxTJTmL15FzjnrV8OGvfUxImWq9hgh9QBRnREia/a7j4X6Hq1t0yux2OxaLFVkldJb0Km1kWofhgE6WujrjeDzS73vONhsO3UBTOZxbcNgPDP2B7ZDIKJpFO428UdbR4/HIfr+X61JV1LUkZI97ybdRKKpv9clKvQU+/8ZfMOP/N3ySc758Fz9ovhdn/BzM9+KMXxW8s3sR5vtxxs/FvDbO+FXBfC/O+FXBvE/P+FXCN7ofvxWJM2PGjBkzZsyYMWPGjBkzZsyYMeOXg19CbsaMGTNmzJgxY8aMGTNmzJgxY8aMb4uZxJkxY8aMGTNmzJgxY8aMGTNmzPgOYCZxZsyYMWPGjBkzZsyYMWPGjBkzvgOYSZwZM2bMmDFjxowZM2bMmDFjxozvAGYSZ8aMGTNmzJgxY8aMGTNmzJgx4zuAmcSZMWPGjBkzZsyYMWPGjBkzZsz4DmAmcWbMmDFjxowZM2bMmDFjxowZM74DmEmcGTNmzJgxY8aMGTNmzJgxY8aM7wBmEmfGjBkzZsyYMWPGjBkzZsyYMeM7gP8LXHzKZ0zAIDMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd6413ad668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## TODO: 在你的电脑上，在步骤6中，至少在6张图片上运行你的算法。\n",
    "## 自由地使用所需的代码单元数吧\n",
    "path = []\n",
    "imgs = []\n",
    "for i in range(7):\n",
    "    path.append('images/image'+str(i+1)+'.jpg')\n",
    "    img = cv2.imread(path[i])\n",
    "    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    imgs.append(cv_rgb)\n",
    "\n",
    "fig = plt.figure(figsize=(20,5))\n",
    "for i in range(7):\n",
    "    ax = fig.add_subplot(1, 7, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(imgs[i])\n",
    "\n",
    "    result = dog_recognition(path[i])\n",
    "    if result != False:\n",
    "        print('  ', result)\n",
    "    else:\n",
    "        print('* This is not dog or human.')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
